18019221
Allo-network drugs: extension of the allosteric drug concept to protein- protein interaction and signaling networks.
Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design.
6960300
The efficiency of multi-target drugs: the network approach might help drug design.
Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single-target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug-design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple, drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug-design paradigm will depend not only on a new generation of computer models to identify the correct multiple targets and their multi-fitting, low-affinity drug candidates but also on more-efficient in vivo testing.
1308158
Drug—target network
The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein–protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration–approved drugs and proteins linked by drug–target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.
1450172
Network pharmacology: the next paradigm in drug discovery.
The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
21562023
A network view of disease and compound screening
The large-scale generation and integration of genomic, proteomic, signalling and metabolomic data are increasingly allowing the construction of complex networks that provide a new framework for understanding the molecular basis of physiological or pathophysiological states. Network-based drug discovery aims to harness this knowledge to investigate and understand the impact of interventions, such as candidate drugs, on the molecular networks that define these states. In this article, we describe how such an approach offers a novel way to understand biology, characterize disease and ultimately develop improved therapies, and discuss the challenges to realizing these goals.
785710
Network analyses in systems pharmacology
Systems pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action as one of its approaches. By considering drug actions and side effects in the context of the regulatory networks within which the drug targets and disease gene products function, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Systems pharmacology can provide new approaches for drug discovery for complex diseases. The integrated approach used in systems pharmacology can allow for drug action to be considered in the context of the whole genome. Network-based studies are becoming an increasingly important tool in understanding the relationships between drug action and disease susceptibility genes. This review discusses how analysis of biological networks has contributed to the genesis of systems pharmacology and how these studies have improved global understanding of drug targets, suggested new targets and approaches for therapeutics, and provided a deeper understanding of the effects of drugs. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of existing medications.
41996270
Unveiling the role of network and systems biology in drug discovery.
Network and systems biology offer a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets, and the effects of modifying them, without losing the key molecular details. Here we review some recent advances in network and systems biology applied to human health, and discuss how they can have a big impact on some of the most interesting areas of drug discovery. In particular, we claim that network biology will play a central part in the development of novel polypharmacology strategies to fight complex multifactorial diseases, where efficacious therapies will need to center on altering entire pathways rather than single proteins. We briefly present new developments in the two areas where we believe network and system biology strategies are more likely to have an immediate contribution: predictive toxicology and drug repurposing.
46279297
Cause-effect relationships in medicine: a protein network perspective.
Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.
9397054
Orthosteric- and allosteric-induced ligand-directed trafficking at GPCRs.
Many orthosteric agonists differentially activate downstream effectors of GPCRs. Such defined induction of signaling has strongly supported the hypothesis termed 'ligand-directed trafficking of receptor signaling' (LDTRS). More recently, subtype-selective GPCR activators, such as allosteric agonists and positive allosteric modulators, have also exhibited the capacity to activate specific signaling pathways. Based on this finding, it may be possible to achieve ligand-specific receptor active states that optimize the biological responses specific to GPCRs. This review discusses recent studies in which both orthosteric and allosteric compounds have been demonstrated to induce LDTRS.
3238508
The network as the target
The conventional target centric model of drug discovery is pinned under the weight of prior success and the traditional problems of safety and efficacy for new molecules. An alternative to target centric drug development is to shift focus to the pathways that mediate both biology and pathophysiology. This method has the advantage of not requiring a priori knowledge of the small molecule target, but also comes with it several challenges including target determination. We suggest extending this notion more broadly across the drug discovery process using quantitative network structure‐activity relationships (QNSAR), and discuss the steps necessary to test the hypothesis that systems biology approaches can be used to improve the drug discovery process. Copyright © 2009 John Wiley & Sons, Inc.
17887601
Network medicine: a network-based approach to human disease
Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
7740137
Allostery in pharmacology: thermodynamics, evolution and design.
This review focuses on basic models of allostery, the ambiguous application of the allosteric term in pharmacology illustrated by receptors, the role of thermodynamics in allosteric mechanisms, evolution and design of allostery. The initial step of ligand activation is closure of the agonist-binding cavity. Large entropy increases accompany the agonist-elicited conformational changes of pentameric ligand-gated ion channels due to cavity closure and rearrangement of transmembrane helices. The effects of point mutations on thermodynamic parameters of binding and function can reveal energetic coupling of neighbouring (and distant) amino acid residues in activation. High-order double-mutant cycle analysis and rate-equilibrium linear free-energy relationships can identify the trajectory and conformational spread of activation. Protein assembly and allostery can be deduced from colocalization and physicochemical principles. Molecular evolution has led from homooligomerization of protomers to heterotropic cooperativity and to allosteric regulation. Examples are discussed such as similar paths of protein (dis)assembly and evolution, irreversible evolution, statistical analysis of sequence homology revealing coevolution, different impacts of adaptation and evolution on hemoglobin, and the flagellar motor switch of bacteria. The driving force of dynamic allostery is associated with funnel-like free energy landscapes of protein binding and shifts in conformational fluctuations upon binding. Allostery can be designed based on our increasing knowledge of natural allosteric mechanisms and evolution. The allosteric principle has been applied for various bio/macro/molecular and signal transduction systems as well as in cognitive sciences.
5141768
Allo-network drugs: harnessing allostery in cellular networks.
Allosteric drugs are increasingly used because they produce fewer side effects. Allosteric signal propagation does not stop at the 'end' of a protein, but may be dynamically transmitted across the cell. We propose here that the concept of allosteric drugs can be broadened to 'allo-network drugs' - whose effects can propagate either within a protein, or across several proteins, to enhance or inhibit specific interactions along a pathway. We posit that current allosteric drugs are a special case of allo-network drugs, and suggest that allo-network drugs can achieve specific, limited changes at the systems level, and in this way can achieve fewer side effects and lower toxicity. Finally, we propose steps and methods to identify allo-network drug targets and sites that outline a new paradigm in systems-based drug design.
18036399
Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework
Abstract Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.
26108392
Is allostery an intrinsic property of all dynamic proteins?
Allostery involves coupling of conformational changes between two widely separated binding sites. The common view holds that allosteric proteins are symmetric oligomers, with each subunit existing in “at least” two conformational states with a different affinity for ligands. Recent observations such as the allosteric behavior of myoglobin, a classical example of a nonallosteric protein, call into question the existing allosteric dogma. Here we argue that all (nonfibrous) proteins are potentially allosteric. Allostery is a consequence of re‐distributions of protein conformational ensembles. In a nonallosteric protein, the binding site shape may not show a concerted second‐site change and enzyme kinetics may not reflect an allosteric transition. Nevertheless, appropriate ligands, point mutations, or external conditions may facilitate a population shift, leading a presumably nonallosteric protein to behave allosterically. In principle, practically any potential drug binding to the protein surface can alter the conformational redistribution. The question is its effectiveness in the redistribution of the ensemble, affecting the protein binding sites and its function. Here, we review experimental observations validating this view of protein allostery. Proteins 2004. © 2004 Wiley‐Liss, Inc.
13783080
Allostery: absence of a change in shape does not imply that allostery is not at play.
Allostery is essential for controlled catalysis, signal transmission, receptor trafficking, turning genes on and off, and apoptosis. It governs the organism's response to environmental and metabolic cues, dictating transient partner interactions in the cellular network. Textbooks taught us that allostery is a change of shape at one site on the protein surface brought about by ligand binding to another. For several years, it has been broadly accepted that the change of shape is not induced; rather, it is observed simply because a larger protein population presents it. Current data indicate that while side chains can reorient and rewire, allostery may not even involve a change of (backbone) shape. Assuming that the enthalpy change does not reverse the free-energy change due to the change in entropy, entropy is mainly responsible for binding.
46221135
Dynamically driven protein allostery
Allosteric interactions are typically considered to proceed through a series of discrete changes in bonding interactions that alter the protein conformation. Here we show that allostery can be mediated exclusively by transmitted changes in protein motions. We have characterized the negatively cooperative binding of cAMP to the dimeric catabolite activator protein (CAP) at discrete conformational states. Binding of the first cAMP to one subunit of a CAP dimer has no effect on the conformation of the other subunit. The dynamics of the system, however, are modulated in a distinct way by the sequential ligand binding process, with the first cAMP partially enhancing and the second cAMP completely quenching protein motions. As a result, the second cAMP binding incurs a pronounced conformational entropic penalty that is entirely responsible for the observed cooperativity. The results provide strong support for the existence of purely dynamics-driven allostery.
6580858
Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms
Classification has proven immensely important in science. Using a classification as proposed here should complement text-book descriptions and assist in understanding how function is performed on the single molecule level within the framework of its complex cellular environment.
46290767
Structural and energetic basis of allostery.
Allostery is a biological phenomenon of fundamental importance in regulation and signaling, and efforts to understand this process have led to the development of numerous models. In spite of individual successes in understanding the structural determinants of allostery in well-documented systems, much less success has been achieved in identifying a set of quantitative and transferable ground rules that provide an understanding of how allostery works. Are there organizing principles that allow us to relate structurally different proteins, or are the determinants of allostery unique to each system? Using an ensemble-based model, we show that allosteric phenomena can be formulated in terms of conformational free energies of the cooperative elements in a protein and the coupling interactions between them. Interestingly, the resulting allosteric ground rules provide a framework to reconcile observations that challenge purely structural models of site-to-site coupling, including (a) allostery in the absence of pathways of structural distortions, (b) allostery in the absence of any structural change, and (c) the ability of allosteric ligands to act as agonists under some circumstances and antagonists under others. The ensemble view of allostery that emerges provides insights into the energetic prerequisites of site-to-site coupling and thus into how allostery works.
9539809
Network properties of protein structures
Protein structures can be studied as complex networks of interacting amino acids. We study proteins of different structural classes from the network perspective. Our results indicate that proteins, regardless of their structural class, show small-world network property. Various network parameters offer insight into the structural organisation of proteins and provide indications of modularity in protein networks.
120148584
Amino acid network within protein
The information regarding the structure of a single protein is encoded in the network of interacting amino acids which are mainly of two types—hydrophobic and hydrophilic. In the present study, we have analysed hydrophobic and hydrophilic amino acids network separately. The results show that both the networks have the `small world' property. The degree distributions for long-range interactions of both the networks have the form of power law with exponential cut-off. Hydrophobic networks have larger average degrees of nodes than hydrophilic networks.
45493158
Identification of side-chain clusters in protein structures by a graph spectral method.
This paper presents a novel method to detect side-chain clusters in protein three-dimensional structures using a graph spectral approach. Protein side-chain interactions are represented by a labeled graph in which the nodes of the graph represent the Cbeta atoms and the edges represent the distance between the Cbeta atoms. The distance information and the non-bonded connectivity of the residues are represented in the form of a matrix called the Laplacian matrix. The constructed matrix is diagonalized and clustering information is obtained from the vector components associated with the second lowest eigenvalue and cluster centers are obtained from the vector components associated with the top eigenvalues. The method uses global information for clustering and a single numeric computation is required to detect clusters of interest. The approach has been adopted here to detect a variety of side-chain clusters and identify the residue which makes the largest number of interactions among the residues forming the cluster (cluster centers). Detecting such clusters and cluster centers are important from a protein structure and folding point of view. The crucial residues which are important in the folding pathway as determined by PhiF values (which is a measure of the effect of a mutation on the stability of the transition state of folding) as obtained from protein engineering methods, can be identified from the vector components corresponding to the top eigenvalues. Expanded clusters are detected near the active and binding site of the protein, supporting the nucleation condensation hypothesis for folding. The method is also shown to detect domains in protein structures and conserved side-chain clusters in topologically similar proteins.
18090150
Uncovering network systems within protein structures.
Traditionally, proteins have been viewed as a construct based on elements of secondary structure and their arrangement in three-dimensional space. In a departure from this perspective we show that protein structures can be modelled as network systems that exhibit small-world, single-scale, and to some degree, scale-free properties. The phenomenological network concept of degrees of separation is applied to three-dimensional protein structure networks and reveals how amino acid residues can be connected to each other within six degrees of separation. This work also illuminates the unique features of protein networks in comparison to other networks currently studied. Recognising that proteins are networks provides a means of rationalising the robustness in the overall three-dimensional fold of a protein against random mutations and suggests an alternative avenue to investigate the determinants of protein structure, function and folding.
15064757
Small-world communication of residues and significance for protein dynamics.
It is not merely the position of residues that is critically important for a protein's function and stability, but also their interactions. We illustrate, by using a network construction on a set of 595 nonhomologous proteins, that regular packing is preserved in short-range interactions, but short average path lengths are achieved through some long-range contacts. Thus, lying between the two extremes of regularity and randomness, residues in folded proteins are distributed according to a "small-world" topology. Using this topology, we show that the core residues have the same local packing arrangements irrespective of protein size. Furthermore, we find that the average shortest path lengths are highly correlated with residue fluctuations, providing a link between the spatial arrangement of the residues and protein dynamics.
39809508
A network representation of protein structures: implications for protein stability.
This study views each protein structure as a network of noncovalent connections between amino acid side chains. Each amino acid in a protein structure is a node, and the strength of the noncovalent interactions between two amino acids is evaluated for edge determination. The protein structure graphs (PSGs) for 232 proteins have been constructed as a function of the cutoff of the amino acid interaction strength at a few carefully chosen values. Analysis of such PSGs constructed on the basis of edge weights has shown the following: 1), The PSGs exhibit a complex topological network behavior, which is dependent on the interaction cutoff chosen for PSG construction. 2), A transition is observed at a critical interaction cutoff, in all the proteins, as monitored by the size of the largest cluster (giant component) in the graph. Amazingly, this transition occurs within a narrow range of interaction cutoff for all the proteins, irrespective of the size or the fold topology. And 3), the amino acid preferences to be highly connected (hub frequency) have been evaluated as a function of the interaction cutoff. We observe that the aromatic residues along with arginine, histidine, and methionine act as strong hubs at high interaction cutoffs, whereas the hydrophobic leucine and isoleucine residues get added to these hubs at low interaction cutoffs, forming weak hubs. The hubs identified are found to play a role in bringing together different secondary structural elements in the tertiary structure of the proteins. They are also found to contribute to the additional stability of the thermophilic proteins when compared to their mesophilic counterparts and hence could be crucial for the folding and stability of the unique three-dimensional structure of proteins. Based on these results, we also predict a few residues in the thermophilic and mesophilic proteins that can be mutated to alter their thermal stability.
15275388
Hydrophobic, hydrophilic, and charged amino acid networks within protein.
The native three-dimensional structure of a single protein is determined by the physicochemical nature of its constituent amino acids. The 20 different types of amino acids, depending on their physicochemical properties, can be grouped into three major classes: hydrophobic, hydrophilic, and charged. The anatomy of the weighted and unweighted networks of hydrophobic, hydrophilic, and charged residues separately for a large number of proteins were studied. Results showed that the average degree of the hydrophobic networks has a significantly larger value than that of hydrophilic and charged networks. The average degree of the hydrophilic networks is slightly higher than that of the charged networks. The average strength of the nodes of hydrophobic networks is nearly equal to that of the charged network, whereas that of hydrophilic networks has a smaller value than that of hydrophobic and charged networks. The average strength for each of the three types of networks varies with its degree. The average strength of a node in a charged network increases more sharply than that of the hydrophobic and hydrophilic networks. Each of the three types of networks exhibits the "small-world" property. Our results further indicate that the all-amino-acids networks and hydrophobic networks are of assortative type. Although most of the hydrophilic and charged networks are of the assortative type, few others have the characteristics of disassortative mixing of the nodes. We have further observed that all-amino-acids networks and hydrophobic networks bear the signature of hierarchy, whereas the hydrophilic and charged networks do not have any hierarchical signature.
7236107
Network analysis of protein dynamics
The network paradigm is increasingly used to describe the topology and dynamics of complex systems. Here, we review the results of the topological analysis of protein structures as molecular networks describing their small‐world character, and the role of hubs and central network elements in governing enzyme activity, allosteric regulation, protein motor function, signal transduction and protein stability. We summarize available data how central network elements are enriched in active centers and ligand binding sites directing the dynamics of the entire protein. We assess the feasibility of conformational and energy networks to simplify the vast complexity of rugged energy landscapes and to predict protein folding and dynamics. Finally, we suggest that modular analysis, novel centrality measures, hierarchical representation of networks and the analysis of network dynamics will soon lead to an expansion of this field.
12454596
Proteins as networks: usefulness of graph theory in protein science.
The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.
25968137
Intra and inter-molecular communications through protein structure network.
Communication within and across proteins is crucial for the biological functioning of proteins. Experiments such as mutational studies on proteins provide important information on the amino acids, which are crucial for their function. However, the protein structures are complex and it is unlikely that the entire responsibility of the function rests on only a few amino acids. A large fraction of the protein is expected to participate in its function at some level or other. Thus, it is relevant to consider the protein structures as a completely connected network and then deduce the properties, which are related to the global network features. In this direction, our laboratory has been engaged in representing the protein structure as a network of non-covalent connections and we have investigated a variety of problems in structural biology, such as the identification of functional and folding clusters, determinants of quaternary association and characterization of the network properties of protein structures. We have also addressed a few important issues related to protein dynamics, such as the process of oligomerization in multimers, mechanism of protein folding, and ligand induced communications (allosteric effect). In this review we highlight some of the investigations which we have carried out in the recent past. A review on protein structure graphs was presented earlier, in which the focus was on the graphs and graph spectral properties and their implementation in the study of protein structure graphs/networks (PSN). In this article, we briefly summarize the relevant parts of the methodology and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks. The investigations of structural/biological problems are divided into two parts, in which the first part deals with the analysis of PSNs based on static structures obtained from x-ray crystallography. The second part highlights the changes in the network, associated with biological functions, which are deduced from the network analysis on the structures obtained from molecular dynamics simulations.
12597133
Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function: hypotheses and a comprehensive review.
During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into 'cumulus-type', i.e., those similar to puffy (white) clouds, and 'stratus-type', i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an 'energy transfer' mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by 'multi-trajectories'; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach 'rarely visited' but functionally-related states. We also show the role of disorder in 'spatial games' of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.
23610642
Interaction energy based protein structure networks.
The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis.
9508014
Network analysis of protein structures identifies functional residues.
Identifying active site residues strictly from protein three-dimensional structure is a difficult task, especially for proteins that have few or no homologues. We transformed protein structures into residue interaction graphs (RIGs), where amino acid residues are graph nodes and their interactions with each other are the graph edges. We found that active site, ligand-binding and evolutionary conserved residues, typically have high closeness values. Residues with high closeness values interact directly or by a few intermediates with all other residues of the protein. Combining closeness and surface accessibility identified active site residues in 70% of 178 representative structures. Detailed structural analysis of specific enzymes also located other types of functional residues. These include the substrate binding sites of acetylcholinesterases and subtilisin, and the regions whose structural changes activate MAP kinase and glycogen phosphorylase. Our approach uses single protein structures, and does not rely on sequence conservation, comparison to other similar structures or any prior knowledge. Residue closeness is distinct from various sequence and structure measures and can thus complement them in identifying key protein residues. Closeness integrates the effect of the entire protein on single residues. Such natural structural design may be evolutionary maintained to preserve interaction redundancy and contribute to optimal setting of functional sites.
10563799
Creative elements: network-based predictions of active centres in proteins and cellular and social networks.
Active centres and hot spots of proteins have a paramount importance in enzyme action, protein-complex formation and drug design. Recently, several publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show several properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks, from molecules to society at large. Using analogy, I propose that existing findings and methodology already enable us to detect active centres in cells and can be expanded to social networks and ecosystems. Members of these active centres are termed here as 'creative elements' of their respective networks, which can help them to survive unprecedented, novel challenges and play a key part in the development, survival and evolvability of complex systems.
38880919
Small‐world network approach to identify key residues in protein–protein interaction
We show that protein complexes can be represented as small‐world networks, exhibiting a relatively small number of highly central amino‐acid residues occurring frequently at protein–protein interfaces. We further base our analysis on a set of different biological examples of protein–protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures. Proteins 2005. © 2004 Wiley‐Liss, Inc.
460503
Insights into the quaternary association of proteins through structure graphs: a case study of lectins.
The unique three-dimensional structure of both monomeric and oligomeric proteins is encoded in their sequence. The biological functions of proteins are dependent on their tertiary and quaternary structures, and hence it is important to understand the determinants of quaternary association in proteins. Although a large number of investigations have been carried out in this direction, the underlying principles of protein oligomerization are yet to be completely understood. Recently, new insights into this problem have been gained from the analysis of structure graphs of proteins belonging to the legume lectin family. The legume lectins are an interesting family of proteins with very similar tertiary structures but varied quaternary structures. Hence they have become a very good model with which to analyse the role of primary structures in determining the modes of quaternary association. The present review summarizes the results of a legume lectin study as well as those obtained from a similar analysis carried out here on the animal lectins, namely galectins, pentraxins, calnexin, calreticulin and rhesus rotavirus Vp4 sialic-acid-binding domain. The lectin structure graphs have been used to obtain clusters of non-covalently interacting amino acid residues at the intersubunit interfaces. The present study, performed along with traditional sequence alignment methods, has provided the signature sequence motifs for different kinds of quaternary association seen in lectins. Furthermore, the network representation of the lectin oligomers has enabled us to detect the residues which make extensive interactions ('hubs') across the oligomeric interfaces that can be targetted for interface-destabilizing mutations. The present review also provides an overview of the methodology involved in representing oligomeric protein structures as connected networks of amino acid residues. Further, it illustrates the potential of such a representation in elucidating the structural determinants of protein-protein association in general and will be of significance to protein chemists and structural biologists.
9552395
Oligomeric protein structure networks: insights into protein-protein interactions
BackgroundProtein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues) with special emphasis to protein interfaces.ResultsA variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb). A few predictions of interface hot spots have also been made based on the results obtained from this analysis, which await experimental verification.ConclusionThe construction and analysis of oligomeric protein structure networks and their comparison with monomeric protein structure networks provide insights into protein association. Further, the interface hubs identified using the present method can be effective targets for interface de-stabilizing mutations. We believe this analysis will significantly enhance our knowledge of the principles behind protein association and also aid in protein design.
14264452
Small-world view of the amino acids that play a key role in protein folding.
We use geometrical considerations to provide a different perspective on the fact that a few selected amino acids, the so-called "key residues," act as nucleation centers for protein folding. By constructing graphs corresponding to protein structures we show that they have the "small-world" feature of having a limited set of vertices with large connectivity. These vertices correspond to the key residues that play the role of "hubs" in the network of interactions that stabilize the structure of the transition state.
5398134
Dynamics of lysozyme structure network: probing the process of unfolding.
Recently we showed that the three-dimensional structure of proteins can be investigated from a network perspective, where the amino acid residues represent the nodes in the network and the noncovalent interactions between them are considered for the edge formation. In this study, the dynamical behavior of such networks is examined by considering the example of T4 lysozyme. The equilibrium dynamics and the process of unfolding are followed by simulating the protein at 300 K and at higher temperatures (400 K and 500 K), respectively. The snapshots of the protein structure from the simulations are represented as protein structure networks in which the strength of the noncovalent interactions is considered an important criterion in the construction of edges. The profiles of the network parameters, such as the degree distribution and the size of the largest cluster (giant component), were examined as a function of interaction strength at different temperatures. Similar profiles are seen at all the temperatures. However, the critical strength of interaction (Icritical) and the size of the largest cluster at all interaction strengths shift to lower values at 500 K. Further, the folding/unfolding transition is correlated with contacts evaluated at Icritical and with the composition of the top large clusters obtained at interaction strengths greater than Icritical. Finally, the results are compared with experiments, and predictions are made about the residues, which are important for stability and folding. To summarize, the network analysis presented in this work provides insights into the details of the changes occurring in the protein tertiary structure at the level of amino acid side-chain interactions, in both the equilibrium and the unfolding simulations. The method can also be employed as a valuable tool in the analysis of molecular dynamics simulation data, since it captures the details at a global level, which may elude conventional pairwise interaction analysis.
15559539
Assortative mixing in Protein Contact Networks and protein folding kinetics
MOTIVATION
Starting from linear chains of amino acids, the spontaneous folding of proteins into their elaborate 3D structures is one of the remarkable examples of biological self-organization. We investigated native state structures of 30 single-domain, two-state proteins, from complex networks perspective, to understand the role of topological parameters in proteins' folding kinetics, at two length scales--as 'Protein Contact Networks (PCNs)' and their corresponding 'Long-range Interaction Networks (LINs)' constructed by ignoring the short-range interactions.


RESULTS
Our results show that, both PCNs and LINs exhibit the exceptional topological property of 'assortative mixing' that is absent in all other biological and technological networks studied so far. We show that the degree distribution of these contact networks is partly responsible for the observed assortativity. The coefficient of assortativity also shows a positive correlation with the rate of protein folding at both short- and long-contact scale, whereas, the clustering coefficients of only the LINs exhibit a negative correlation. The results indicate that the general topological parameters of these naturally evolved protein networks can effectively represent the structural and functional properties required for fast information transfer among the residues facilitating biochemical/kinetic functions, such as, allostery, stability and the rate of folding.


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
33192268
Construction and application of the weighted amino acid network based on energy.
A method is proposed to construct the weighted amino acid network. The weight of the link is based on the contact energy between residues. For the 197 proteins with low homology, the "small-world" property was studied based on this method. Additionally, analyses were carried out for the statistic characteristics of the network parameters, the influence of the weight on the network parameters, the network parameter difference of amino acids, and the links between the hydrophobic and hydrophilic residues. Using this method, we studied the network parameter change for the protein chymotrypsin inhibitor 2 (CI2) on its high-temperature unfolding pathway. It is found that the unfolding of the protein is mainly exhibited as the derogation of the hydrophobic core and the shortest path length rise in the unfolding process. This work is helpful for studies of protein folding and the relationship between structure and function using complex network theory.
40792441
Screened nonbonded interactions in native proteins manipulate optimal paths for robust residue communication.
A protein structure is represented as a network of residues whereby edges are determined by intramolecular contacts. We introduce inhomogeneity into these networks by assigning each edge a weight that is determined by amino acid pair potentials. Two methodologies are utilized to calculate the average path lengths (APLs) between pairs: to minimize i), the maximum weight in the strong APL, and ii), the total weight in the weak APL. We systematically screen edges that have higher than a cutoff potential and calculate the shortest APLs in these reduced networks, while keeping chain connectivity. Therefore, perturbations introduced at a selected region of the residue network propagate to remote regions only along the nonscreened edges that retain their ability to disseminate the perturbation. The shortest APLs computed from the reduced homogeneous networks with only the strongest few nonbonded pairs closely reproduce the strong APLs from the weighted networks. The rate of change in the APL in the reduced residue network as compared to its randomly connected counterpart remains constant until a lower bound. Upon further link removal, this property shows an abrupt increase toward a random coil behavior. Under different perturbation scenarios, diverse optimal paths emerge for robust residue communication.
28228019
A study of communication pathways in methionyl- tRNA synthetase by molecular dynamics simulations and structure network analysis
The enzymes of the family of tRNA synthetases perform their functions with high precision by synchronously recognizing the anticodon region and the aminoacylation region, which are separated by ≈70 Å in space. This precision in function is brought about by establishing good communication paths between the two regions. We have modeled the structure of the complex consisting of Escherichia coli methionyl-tRNA synthetase (MetRS), tRNA, and the activated methionine. Molecular dynamics simulations have been performed on the modeled structure to obtain the equilibrated structure of the complex and the cross-correlations between the residues in MetRS have been evaluated. Furthermore, the network analysis on these simulated structures has been carried out to elucidate the paths of communication between the activation site and the anticodon recognition site. This study has provided the detailed paths of communication, which are consistent with experimental results. Similar studies also have been carried out on the complexes (MetRS + activated methonine) and (MetRS + tRNA) along with ligand-free native enzyme. A comparison of the paths derived from the four simulations clearly has shown that the communication path is strongly correlated and unique to the enzyme complex, which is bound to both the tRNA and the activated methionine. The details of the method of our investigation and the biological implications of the results are presented in this article. The method developed here also could be used to investigate any protein system where the function takes place through long-distance communication.
34000037
Predicting allosteric communication in myosin via a pathway of conserved residues.
We present a computational method that predicts a pathway of residues that mediate protein allosteric communication. The pathway is predicted using only a combination of distance constraints between contiguous residues and evolutionary data. We applied this analysis to find pathways of conserved residues connecting the myosin ATP binding site to the lever arm. These pathway residues may mediate the allosteric communication that couples ATP hydrolysis to the lever arm recovery stroke. Having examined pre-stroke conformations of Dictyostelium, scallop, and chicken myosin II as well as Dictyostelium myosin I, we observed a conserved pathway traversing switch II and the relay helix, which is consistent with the understood need for allosteric communication in this conformation. We also examined post-rigor and rigor conformations across several myosin species. Although initial residues of these paths are more heterogeneous, all but one of these paths traverse a consistent set of relay helix residues to reach the beginning of the lever arm. We discuss our results in the context of structural elements and reported mutational experiments, which substantiate the significance of the pre-stroke pathways. Our method provides a simple, computationally efficient means of predicting a set of residues that mediate allosteric communication. We provide a refined, downloadable application and source code (on https://simtk.org) to share this tool with the wider community (https://simtk.org/home/allopathfinder).
13542355
Dynamical networks in tRNA:protein complexes
Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNAGlu and an archaeal leucyl-tRNA synthetase (LeuRS):tRNALeu complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
205841370
Dimerization and ligand binding affect the structure network of A(2A) adenosine receptor.
G protein Coupled Receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. The representation of GPCR structures as networks of interacting amino acids can be a meaningful way to decipher the impact of ligand and of dimerization/oligomerization on the molecular communication intrinsic to the protein fold. In this study, we predicted likely homodimer architectures of the A(2A)R and investigated the effects of dimerization on the structure network and the communication paths of the monomeric form. The results of this study emphasize the roles of helix 1 in A(2A)R dimerization and of highly conserved amino acids in helices 1, 2, 6 and 7 in maintaining the structure network of the A(2A)R through a persistent hub behavior as well as in the information flow between the extracellular and intracellular poles of the helix bundle. The arginine of the conserved E/DRY motif, R3.50, is not involved in the communication paths but participates in the structure network as a stable hub, being linked to both D3.49 and E6.30 like in the inactive states of rhodopsin. A(2A)R dimerization affects the communication networks intrinsic to the receptor fold in a way dependent on the dimer architecture. Certain architectures retain the most recurrent communication paths with respect to the monomeric antagonist-bound form but enhancing path numbers and frequencies, whereas some others impair ligand-mediated communication networks. Ligand binding affects the network as well. Overall, the communication network that pertains to the functional dynamics of a GPCR is expected to be influenced by ligand functionality, oligomeric order and architecture of the supramolecular assembly.
7838743
Conserved amino acids participate in the structure networks deputed to intramolecular communication in the lutropin receptor
The luteinizing hormone receptor (LHR) is a G protein-coupled receptor (GPCR) particularly susceptible to spontaneous pathogenic gain-of-function mutations. Protein structure network (PSN) analysis on wild-type LHR and two constitutively active mutants, combined with in vitro mutational analysis, served to identify key amino acids that are part of the regulatory network responsible for propagating communication between the extracellular and intracellular poles of the receptor. Highly conserved amino acids in the rhodopsin family GPCRs participate in the protein structural stability as network hubs in both the inactive and active states. Moreover, they behave as the most recurrent nodes in the communication paths between the extracellular and intracellular sides in both functional states with emphasis on the active one. In this respect, non-conservative loss-of-function mutations of these amino acids is expected to impair the most relevant way of communication between activating mutation sites or hormone-binding domain and G protein recognition regions.
11408296
Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics
Allostery offers a highly specific way to modulate protein function. Therefore, understanding this mechanism is of increasing interest for protein science and drug discovery. However, allosteric signal transmission is difficult to detect experimentally and to model because it is often mediated by local structural changes propagating along multiple pathways. To address this, we developed a method to identify communication pathways by an information‐theoretical analysis of molecular dynamics simulations. Signal propagation was described as information exchange through a network of correlated local motions, modeled as transitions between canonical states of protein fragments. The method was used to describe allostery in two‐component regulatory systems. In particular, the transmission from the allosteric site to the signaling surface of the receiver domain NtrC was shown to be mediated by a layer of hub residues. The location of hubs preferentially connected to the allosteric site was found in close agreement with key residues experimentally identified as involved in the signal transmission. The comparison with the networks of the homologues CheY and FixJ highlighted similarities in their dynamics. In particular, we showed that a preorganized network of fragment connections between the allosteric and functional sites exists already in the inactive state of all three proteins.—Pandini, A., Fornili, A., Fraternali, F., Kleinjung, J. Detection of allosteric signal transmission by information‐theoretic analysis of protein dynamics. FASEB J. 26, 868–881 (2012). www.fasebj.org
15368544
Danger in the dust.
Allosteric communications are vital for cellular signaling. Here we explore a relationship between protein architectural organization and shortcuts in signaling pathways. We show that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways. These mediating residues tend to be located at the inter-modular boundaries, which are more rigid and display a larger number of long-range interactions than intra-modular regions. The inter-modular boundaries contain most of the residues centrally conserved in the protein fold, which may be crucial for information transfer between amino acids. Our approach to modular decomposition relies on a representation of protein structures as residue-interacting networks, and removal of the most central residue contacts, which are assumed to be crucial for allosteric communications. The modular decomposition of 100 multi-domain protein structures indicates that modules constitute the building blocks of domains. The analysis of 13 allosteric proteins revealed that modules characterize experimentally identified functional regions. Based on the study of an additional functionally annotated dataset of 115 proteins, we propose that high-modularity modules include functional sites and are the basic functional units. We provide examples (the Gα s subunit and P450 cytochromes) to illustrate that the modular architecture of active sites is linked to their functional specialization. Our method decomposes protein structures into modules, allowing the study of signal transmission between functional sites. A modular configuration might be advantageous: it allows signaling proteins to expand their regulatory linkages and may elicit a broader range of control mechanisms either via modular combinations or through modulation of inter-modular linkages.
9568295
Stability of graph communities across time scales
The complexity of biological, social, and engineering networks makes it desirable to find natural partitions into clusters (or communities) that can provide insight into the structure of the overall system and even act as simplified functional descriptions. Although methods for community detection abound, there is a lack of consensus on how to quantify and rank the quality of partitions. We introduce here the stability of a partition, a measure of its quality as a community structure based on the clustered autocovariance of a dynamic Markov process taking place on the network. Because the stability has an intrinsic dependence on time scales of the graph, it allows us to compare and rank partitions at each time and also to establish the time spans over which partitions are optimal. Hence the Markov time acts effectively as an intrinsic resolution parameter that establishes a hierarchy of increasingly coarser communities. Our dynamical definition provides a unifying framework for several standard partitioning measures: modularity and normalized cut size can be interpreted as one-step time measures, whereas Fiedler’s spectral clustering emerges at long times. We apply our method to characterize the relevance of partitions over time for constructive and real networks, including hierarchical graphs and social networks, and use it to obtain reduced descriptions for atomic-level protein structures over different time scales.
14487889
Protein multi-scale organization through graph partitioning and robustness analysis: application to the myosin-myosin light chain interaction.
Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.
230241
ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality
UNLABELLED
The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks.


AVAILABILITY
The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php.
21128936
Exploring residue component contributions to dynamical network models of allostery.
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue alpha-carbons, (ii) sidechain center of mass, (iii) backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g. a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan-Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
7677365
Evolutionarily conserved pathways of energetic connectivity in protein families.
For mapping energetic interactions in proteins, a technique was developed that uses evolutionary data for a protein family to measure statistical interactions between amino acid positions. For the PDZ domain family, this analysis predicted a set of energetically coupled positions for a binding site residue that includes unexpected long-range interactions. Mutational studies confirm these predictions, demonstrating that the statistical energy function is a good indicator of thermodynamic coupling in proteins. Sets of interacting residues form connected pathways through the protein fold that may be the basis for efficient energy conduction within proteins.
40289209
Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations
An interesting example of an allosteric protein is the chaperonin GroEL. It undergoes adenosine 5′‐triphosphate‐induced conformational changes that are reflected in binding of adenosine 5′‐triphosphate with positive cooperativity within rings and negative cooperativity between rings. Herein, correlated mutations in chaperonins are analyzed to unravel routes of allosteric communication in GroEL and in its complex with its co‐chaperonin GroES. It is shown that analysis of correlated mutations in the chaperonin family can provide information about pathways of allosteric communication within GroEL and between GroEL and GroES. The results are discussed in the context of available structural, genetic, and biochemical data concerning short‐ and long‐range interactions in the GroE system. Proteins 2002;48:611–617. © 2002 Wiley‐Liss, Inc.
7253565
Evolutionarily conserved networks of residues mediate allosteric communication in proteins
A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Here, we describe a sequence-based statistical method for quantitatively mapping the global network of amino acid interactions in a protein. Application of this method for three structurally and functionally distinct protein families (G protein-coupled receptors, the chymotrypsin class of serine proteases and hemoglobins) reveals a surprisingly simple architecture for amino acid interactions in each protein family: a small subset of residues forms physically connected networks that link distant functional sites in the tertiary structure. Although small in number, residues comprising the network show excellent correlation with the large body of mechanistic data available for each family. The data suggest that evolutionarily conserved sparse networks of amino acid interactions represent structural motifs for allosteric communication in proteins.
25474239
Allosteric determinants in guanine nucleotide-binding proteins
Members of the G protein superfamily contain nucleotide-dependent switches that dictate the specificity of their interactions with binding partners. Using a sequence-based method termed statistical coupling analysis (SCA), we have attempted to identify the allosteric core of these proteins, the network of amino acid residues that couples the domains responsible for nucleotide binding and protein–protein interactions. One-third of the 38 residues identified by SCA were mutated in the G protein Gsα, and the interactions of guanosine 5′-3-O-(thio)triphosphate- and GDP-bound mutant proteins were tested with both adenylyl cyclase (preferential binding to GTP-Gsα) and the G protein βγ subunit complex (preferential binding to GDP-Gsα). A two-state allosteric model predicts that mutation of residues that control the equilibrium between GDP- and GTP-bound conformations of the protein will cause the ratio of affinities of these species for adenylyl cyclase and Gβγ to vary in a reciprocal fashion. Observed results were consistent with this prediction. The network of residues identified by the SCA appears to comprise a core allosteric mechanism conferring nucleotide-dependent switching; the specific features of different G protein family members are built on this core.
1465778
Signal transduction pathway of TonB-dependent transporters
Transcription of the ferric citrate import system is regulated by ferric citrate binding to the outer membrane transporter FecA. A signal indicating transporter occupancy is relayed across the outer membrane to energy-transducing and regulatory proteins embedded in the cytoplasmic membrane. Because transcriptional activation is not coupled to ferric citrate import, an allosteric mechanism underlies this complex signaling mechanism. Using evolution-based statistical analysis we have identified a sparse but structurally connected network of residues that links distant functional sites in FecA. Functional analyses of these positions confirm their involvement in the mechanism that regulates transcriptional activation in response to ferric citrate binding at the cell surface. This mechanism appears to be conserved and provides the structural basis for the allosteric signaling of TonB-dependent transporters.
4324647
The spatial architecture of protein function and adaptation
Statistical analysis of protein evolution suggests a design for natural proteins in which sparse networks of coevolving amino acids (termed sectors) comprise the essence of three-dimensional structure and function. However, proteins are also subject to pressures deriving from the dynamics of the evolutionary process itself—the ability to tolerate mutation and to be adaptive to changing selection pressures. To understand the relationship of the sector architecture to these properties, we developed a high-throughput quantitative method for a comprehensive single-mutation study in which every position is substituted individually to every other amino acid. Using a PDZ domain (PSD95pdz3) model system, we show that sector positions are functionally sensitive to mutation, whereas non-sector positions are more tolerant to substitution. In addition, we find that adaptation to a new binding specificity initiates exclusively through variation within sector residues. A combination of just two sector mutations located near and away from the ligand-binding site suffices to switch the binding specificity of PSD95pdz3 quantitatively towards a class-switching ligand. The localization of functional constraint and adaptive variation within the sector has important implications for understanding and engineering proteins.
23657698
Ligand-dependent dynamics and intramolecular signaling in a PDZ domain.
Allosteric communication is a fundamental process that proteins use to propagate signals from one site to functionally important distal sites. Although allostery is usually associated with multimeric proteins and enzymes, "long-range" communication may be a fundamental property of proteins. In some cases, communication occurs with minimal structural change. PDZ (post-synaptic density-95/discs large/zonula occludens-1) domains are small, protein-protein binding modules that can use multiple surfaces for docking diverse molecules. Furthermore, these domains have long-range energetic couplings that link the ligand-binding site to distal regions of the structure. Here, we show that allosteric behavior in a representative member of the PDZ domain family may be directly detected using side-chain methyl dynamics measurements. The changes in side-chain dynamics parameters in the second PDZ domain from the human tyrosine phosphatase 1E (hPTP1E) were determined upon binding a peptide target. Long-range dynamic effects were detected that correspond to previously observed pair-wise energetic couplings. These results provide one of the first experimental examples for the potential role of ps-ns timescale dynamics in propagating long-range signals within a protein, and reinforce the idea that dynamic fluctuations in proteins contribute to allosteric signal transduction.
18750520
On Evolutionary Conservation of Thermodynamic Coupling in Proteins*
The inherent complexity of thermodynamic coupling in proteins presents a major challenge in understanding and engineering protein function. Recent work has argued that the study of proteins can be simplified by the use of correlated mutations in the evolutionary record to locate a small subset of thermodynamically coupled residues that participate in functionally important, evolutionarily conserved energetic pathways. To test this hypothesis, we examined the predictions of correlated mutation algorithms for a number of proteins for which coupling between residues has been determined by analysis of double mutant cycles. We find that correlated mutation algorithms can find residue pairs that are physically close and that physically close residue pairs tend to be thermodynamically coupled. We find little evidence, however, for the hypothesis that thermodynamic coupling is limited to the subset of evolutionarily constrained residue positions.
24183868
Reassessing a sparse energetic network within a single protein domain
Understanding the molecular principles that govern allosteric communication is an important goal in protein science. One way allostery could be transmitted is via sparse energetic networks of residues, and one such evolutionary conserved network was identified in the PDZ domain family of proteins by multiple sequence alignment [Lockless SW, Ranganathan R (1999) Science 286:295–299]. We have reassessed the energetic coupling of these residues by double mutant cycles together with ligand binding and stability experiments and found that coupling is not a special property of the coevolved network of residues in PDZ domains. The observed coupling for ligand binding is better explained by a distance relationship, where residues close in space are more likely to couple than distal residues. Our study demonstrates that statistical coupling from sequence analysis is not necessarily a reporter of energetic coupling and allostery.
13751589
On the accuracy of inferring energetic coupling between distant sites in protein families from evolutionary imprints: Illustrations using lattice model
It is suspected that correlated motions among a subset of spatially separated residues drive conformational dynamics not only in multidomain but also in single domain proteins. Sequence and structure‐based methods have been proposed to determine covariation between two sites on a protein. The statistical coupling analysis (SCA) that compares the changes in probability at two sites in a multiple sequence alignment (MSA) and a subset of the MSA has been used to infer the network of residues that encodes allosteric signals in protein families. The structural perturbation method (SPM), that probes the response of a local perturbation at all other sites, has been used to probe the allostery wiring diagram in biological machines and enzymes. To assess the efficacy of the SCA, we used an exactly soluble two dimensional lattice model and performed double‐mutant cycle (DMC) calculations to predict the extent of physical coupling between two sites. The predictions of the SCA and the DMC results show that only residues that are in contact in the native state are accurately identified. In addition, covariations among strongly interacting residues are most easily identified by the SCA. These conclusions are consistent with the DMC experiments on the PDZ family. Good correlation between the SCA and the DMC is only obtained by performing multiple experiments that vary the nature of amino acids at a given site. In contrast, the energetic coupling found in experiments for the PDZ domain are recovered using the SPM. We also predict, using the SPM, several residues that are coupled energetically. Proteins 2009. © 2009 Wiley‐Liss, Inc.
14334765
Allosteric communication in dihydrofolate reductase: signaling network and pathways for closed to occluded transition and back.
Escherichia coli dihydrofolate reductase (DHFR) catalyzes the reduction of dihydrofolate to tetrahydrofolate. During the catalytic cycle, DHFR undergoes conformational transitions between the closed (CS) and occluded (OS) states that, respectively, describe whether the active site is closed or occluded by the Met20 loop. The CS-->OS and the reverse transition may be viewed as allosteric transitions. Using a sequence-based approach, we identify a network of residues that represents the allostery wiring diagram. Many of the residues in the allostery wiring diagram, which are dispersed throughout the adenosine-binding domain as well as the loop domain, are not conserved. Several of the residues in the network have been previously shown by NMR experiments, mutational studies, and molecular dynamics simulations to be linked to equilibration conformational fluctuations of DHFR. To further probe the nature of events that occur during conformational fluctuations, we use a self-organized polymer model to monitor the kinetics of the CS-->OS and the reverse transitions. During the CS-->OS transition, coordinated changes in a number of residues in the loop domain enable the Met20 loop to slide along the alpha-helix in the adenosine-binding domain. Sliding is triggered by pulling of the Met20 loop by the betaG-betaH loop and the pushing action of the betaG-betaH loop. The residues that facilitate the Met20 loop motion are part of the network of residues that transmit allosteric signals during the CS-->OS transition. Replacement of M16 and G121, whose C(alpha) atoms are about 4.3 A in the CS, by a disulfide cross-link impedes that CS-->OS transition. The order of events in the OS-->CS transition is not the reverse of the forward transition. The contact Glu18-Ser49 in the OS persists until the sliding of the Met20 loop is nearly complete. The ensemble of structures in the transition state in both the allosteric transitions is heterogeneous. The most probable transition-state structure resembles the OS (CS) in the CS-->OS (OS-->CS) transition, which is in accord with the Hammond postulate. Structures resembling the OS (CS) are present as minor ( approximately 1-3%) components in equilibrated CS (OS) structures.
8194145
Exploring allosteric coupling in the α-subunit of Heterotrimeric G proteins using evolutionary and ensemble-based approaches
BackgroundAllosteric coupling, which can be defined as propagation of a perturbation at one region of the protein molecule (such as ligand binding) to distant sites in the same molecule, constitutes the most general mechanism of regulation of protein function. However, unlike molecular details of ligand binding, structural elements involved in allosteric effects are difficult to diagnose. Here, we identified allosteric linkages in the α-subunits of heterotrimeric G proteins, which were evolved to transmit membrane receptor signals by allosteric mechanisms, by using two different approaches that utilize fundamentally different and independent information.ResultsWe analyzed: 1) correlated mutations in the family of G protein α-subunits, and 2) cooperativity of the native state ensemble of the Gαi1 or transducin. The combination of these approaches not only recovered already-known details such as the switch regions that change conformation upon nucleotide exchange, and those regions that are involved in receptor, effector or Gβγ interactions (indicating that the predictions of the analyses can be viewed with a measure of confidence), but also predicted new sites that are potentially involved in allosteric communication in the Gα protein. A summary of the new sites found in the present analysis, which were not apparent in crystallographic data, is given along with known functional and structural information. Implications of the results are discussed.ConclusionA set of residues and/or structural elements that are potentially involved in allosteric communication in Gα is presented. This information can be used as a guide to structural, spectroscopic, mutational, and theoretical studies on the allosteric network in Gα proteins, which will provide a better understanding of G protein-mediated signal transduction.
5604898
Surface Sites for Engineering Allosteric Control in Proteins
Statistical analyses of protein families reveal networks of coevolving amino acids that functionally link distantly positioned functional surfaces. Such linkages suggest a concept for engineering allosteric control into proteins: The intramolecular networks of two proteins could be joined across their surface sites such that the activity of one protein might control the activity of the other. We tested this idea by creating PAS-DHFR, a designed chimeric protein that connects a light-sensing signaling domain from a plant member of the Per/Arnt/Sim (PAS) family of proteins with Escherichia coli dihydrofolate reductase (DHFR). With no optimization, PAS-DHFR exhibited light-dependent catalytic activity that depended on the site of connection and on known signaling mechanisms in both proteins. PAS-DHFR serves as a proof of concept for engineering regulatory activities into proteins through interface design at conserved allosteric sites.
17336112
Hot Spots for Allosteric Regulation on Protein Surfaces
Recent work indicates a general architecture for proteins in which sparse networks of physically contiguous and coevolving amino acids underlie basic aspects of structure and function. These networks, termed sectors, are spatially organized such that active sites are linked to many surface sites distributed throughout the structure. Using the metabolic enzyme dihydrofolate reductase as a model system, we show that: (1) the sector is strongly correlated to a network of residues undergoing millisecond conformational fluctuations associated with enzyme catalysis, and (2) sector-connected surface sites are statistically preferred locations for the emergence of allosteric control in vivo. Thus, sectors represent an evolutionarily conserved "wiring" mechanism that can enable perturbations at specific surface positions to rapidly initiate conformational control over protein function. These findings suggest that sectors enable the evolution of intermolecular communication and regulation.
13501612
Principles underlying energetic coupling along an allosteric communication trajectory of a voltage-activated K+ channel
The information flow between distal elements of a protein may rely on allosteric communication trajectories lying along the protein's tertiary or quaternary structure. To unravel the underlying features of energy parsing along allosteric pathways in voltage-gated K+ channels, high-order thermodynamic coupling analysis was performed. We report that such allosteric trajectories are functionally conserved and delineated by well defined boundaries. Moreover, allosteric trajectories assume a hierarchical organization whereby increasingly stronger layers of cooperative residue interactions act to ensure efficient and cooperative long-range coupling between distal channel regions. Such long-range communication is brought about by a coupling of local and global conformational changes, suggesting that the allosteric trajectory also corresponds to a pathway of physical deformation. Supported by theoretical analyses and analogy to studies analyzing the contribution of long-range residue coupling to protein stability, we propose that such experimentally derived trajectory features are a general property of allosterically regulated proteins.
206517816
Trapping Moving Targets with Small Molecules
Structure-based drug design traditionally uses static protein models as inspirations for focusing on “active” site targets. Allosteric regulation of biological macromolecules, however, is affected by both conformational and dynamic properties of the protein or protein complex and can potentially lead to more avenues for therapeutic development. We discuss the advantages of searching for molecules that conformationally trap a macromolecule in its inactive state. Although multiple methodologies exist to probe protein dynamics and ligand binding, our current discussion highlights the use of nuclear magnetic resonance spectroscopy in the drug discovery and design process.
23854592
Allosteric signal transmission in the nucleotide-binding domain of 70-kDa heat shock protein (Hsp70) molecular chaperones
The 70-kDa heat shock protein (Hsp70) chaperones perform a wide array of cellular functions that all derive from the ability of their N-terminal nucleotide-binding domains (NBDs) to allosterically regulate the substrate affinity of their C-terminal substrate-binding domains in a nucleotide-dependent mechanism. To explore the structural origins of Hsp70 allostery, we performed NMR analysis on the NBD of DnaK, the Escherichia coli Hsp70, in six different states (ligand-bound or apo) and in two constructs, one that retains the conserved and functionally crucial portion of the interdomain linker (residues ) and another that lacks the linker. Chemical-shift perturbation patterns identify residues at subdomain interfaces that constitute allosteric networks and enable the NBD to act as a nucleotide-modulated switch. Nucleotide binding results in changes in subdomain orientations and long-range perturbations along subdomain interfaces. In particular, our findings provide structural details for a key mechanism of Hsp70 allostery, by which information is conveyed from the nucleotide-binding site to the interdomain linker. In the presence of ATP, the linker binds to the edge of the IIA β-sheet, which structurally connects the linker and the nucleotide-binding site. Thus, a pathway of allosteric communication leads from the NBD nucleotide-binding site to the substrate-binding domain via the interdomain linker.
18984315
Dynamic coupling and allosteric behavior in a nonallosteric protein.
Long-range intraprotein interactions give rise to many important protein behaviors. Understanding how energy is transduced through protein structures to either transmit a signal or elicit conformational changes is therefore a current challenge in structural biology. In an effort to understand such linkages, multiple V --> A mutations were made in the small globular protein eglin c. The physical responses, as mapped by NMR spin relaxation, residual dipolar couplings (RDCs), and scalar couplings, illustrate that the interior of this nonallosteric protein forms a dynamic network and that local perturbations are transmitted as dynamic and structural changes to distal sites as far as 16 A away. Two basic types of propagation responses were observed: contiguous pathways of enhanced (attenuated) dynamics with no change in structure; and dispersed (noncontiguous) changes in methyl rotation rates that appear to result from subtle deformation of backbone structure. In addition, energy transmission is found to be unidirectional. In one mutant, an allosteric conformational change of a side chain is seen in the context of a pathway of propagated changes in picosecond to nanosecond dynamics. The observation of so many long-range interactions in a small, rigid system lends experimental weight to the idea that all well-folded proteins inherently possess allosteric features [Gunasekaran et al. (2004) Proteins 57, 433-443], and that dynamics are a rich source of information for mapping and gaining mechanistic insight into communication pathways in individual proteins.
24185730
Mapping allostery through the covariance analysis of NMR chemical shifts
Allostery is a fundamental mechanism of regulation in biology. The residues at the end points of long-range allosteric perturbations are commonly identified by the comparative analyses of structures and dynamics in apo and effector-bound states. However, the networks of interactions mediating the propagation of allosteric signals between the end points often remain elusive. Here we show that the covariance analysis of NMR chemical shift changes caused by a set of covalently modified analogs of the allosteric effector (i.e., agonists and antagonists) reveals extended networks of coupled residues. Unexpectedly, such networks reach not only sites subject to effector-dependent structural variations, but also regions that are controlled by dynamically driven allostery. In these regions the allosteric signal is propagated mainly by dynamic rather than structural modulations, which result in subtle but highly correlated chemical shift variations. The proposed chemical shift covariance analysis (CHESCA) identifies interresidue correlations based on the combination of agglomerative clustering (AC) and singular value decomposition (SVD). AC results in dendrograms that define functional clusters of coupled residues, while SVD generates score plots that provide a residue-specific dissection of the contributions to binding and allostery. The CHESCA approach was validated by applying it to the cAMP-binding domain of the exchange protein directly activated by cAMP (EPAC) and the CHESCA results are in full agreement with independent mutational data on EPAC activation. Overall, CHESCA is a generally applicable method that utilizes a selected chemical library of effector analogs to quantitatively decode the binding and allosteric information content embedded in chemical shift changes.
16434956
The Auto-Inhibitory Role of the EPAC Hinge Helix as Mapped by NMR
The cyclic-AMP binding domain (CBD) is the central regulatory unit of exchange proteins activated by cAMP (EPAC). The CBD maintains EPAC in a state of auto-inhibition in the absence of the allosteric effector, cAMP. When cAMP binds to the CBD such auto-inhibition is released, leading to EPAC activation. It has been shown that a key feature of such cAMP-dependent activation process is the partial destabilization of a structurally conserved hinge helix at the C-terminus of the CBD. However, the role of this helix in auto-inhibition is currently not fully understood. Here we utilize a series of progressive deletion mutants that mimic the hinge helix destabilization caused by cAMP to show that such helix is also a pivotal auto-inhibitory element of apo-EPAC. The effect of the deletion mutations on the auto-inhibitory apo/inactive vs. apo/active equilibrium was evaluated using recently developed NMR chemical shift projection and covariance analysis methods. Our results show that, even in the absence of cAMP, the C-terminal region of the hinge helix is tightly coupled to other conserved allosteric structural elements of the CBD and perturbations that destabilize the hinge helix shift the auto-inhibitory equilibrium toward the apo/active conformations. These findings explain the apparently counterintuitive observation that cAMP binds more tightly to shorter than longer EPAC constructs. These results are relevant for CBDs in general and rationalize why substrates sensitize CBD-containing systems to cAMP. Furthermore, the NMR analyses presented here are expected to be generally useful to quantitatively evaluate how mutations affect conformational equilibria.
12094123
The origin of allosteric functional modulation: multiple pre-existing pathways.
Although allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications, and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the observed functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.
2909660
Contact rearrangements form coupled networks from local motions in allosteric proteins
Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that networks of contacts altered, formed, or broken are a significant contributor to allosteric communication in proteins. In this work, we identify which interactions change significantly between the residue–residue contact networks of two allosteric structures, and then organize these changes into graphs. We perform the analysis on 15 pairs of allosteric structures with effector and substrate each present in at least one of the two structures. Most proteins exhibit large, dense regions of contact rearrangement, and the graphs form connected paths between allosteric effector and substrate sites in five of these proteins. In the remaining 10 proteins, large‐scale conformational changes such as rigid‐body motions are likely required in addition to contact rearrangement networks to account for substrate–effector communication. On average, clusters which contain at least one substrate or effector molecule comprise 20% of the protein. These allosteric graphs are small worlds; that is, they typically have mean shortest path lengths comparable to those of corresponding random graphs and average clustering coefficients enhanced relative to those of random graphs. The networks capture 60–80% of known allostery‐perturbing mutants in three proteins, and the metrics degree and closeness are statistically good discriminators of mutant residues from nonmutant residues within the networks in two of these three proteins. For two proteins, coevolving clusters of residues which have been hypothesized to be allosterically important differ from the regions with the most contact rearrangement. Residues and contacts which modulate normal mode fluctuations also often participate in the contact rearrangement networks. In summary, residue–residue contact rearrangement networks provide useful representations of the portions of allosteric pathways resulting from coupled local motions. Proteins 2008. © 2007 Wiley‐Liss, Inc.
6150426
Allosteric Communication Occurs via Networks of Tertiary and Quaternary Motions in Proteins
Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that allosteric communication in proteins relies upon networks of quaternary (collective, rigid-body) and tertiary (residue–residue contact) motions. We argue that cyclic topology of these networks is necessary for allosteric communication. An automated algorithm identifies rigid bodies from the displacement between the inactive and the active structures and constructs “quaternary networks” from these rigid bodies and the substrate and effector ligands. We then integrate quaternary networks with a coarse-grained representation of contact rearrangements to form “global communication networks” (GCNs). The GCN reveals allosteric communication among all substrate and effector sites in 15 of 18 multidomain and multimeric proteins, while tertiary and quaternary networks exhibit such communication in only 4 and 3 of these proteins, respectively. Furthermore, in 7 of the 15 proteins connected by the GCN, 50% or more of the substrate-effector paths via the GCN are “interdependent” paths that do not exist via either the tertiary or the quaternary network. Substrate-effector “pathways” typically are not linear but rather consist of polycyclic networks of rigid bodies and clusters of rearranging residue contacts. These results argue for broad applicability of allosteric communication based on structural changes and demonstrate the utility of the GCN. Global communication networks may inform a variety of experiments on allosteric proteins as well as the design of allostery into non-allosteric proteins.
205706622
Coupling between global dynamics and signal transduction pathways: a mechanism of allostery for chaperonin GroEL.
Despite significant efforts toward understanding the molecular basis of allosteric communication, the mechanisms by which local energetic and conformational changes cooperatively diffuse from ligand-binding sites to distal regions across the 3-dimensional structure of allosteric proteins remain to be established. Recent experimental and theoretical evidence supports the view that allosteric communication is facilitated by the intrinsic ability of the biomolecules to undergo collective changes in structure, triggered by ligand binding. Two groups of studies recently proved to provide insights into such intrinsic, structure-induced effects: elastic network models that permit us to visualize the cooperative changes in conformation that are most readily accessible near native state conditions, and information-theoretic approaches that elucidate the most efficient pathways of signal transmission favored by the overall architecture. Using a combination of these two approaches, we highlight, by way of application to the bacterial chaperonin complex GroEL-GroES, how the most cooperative modes of motion play a role in mediating the propagation of allosteric signals. A functional coupling between the global dynamics sampled under equilibrium conditions and the signal transduction pathways inherently favored by network topology appears to control allosteric effects.
15366701
Fluctuation dynamics analysis of gp120 envelope protein reveals a topologically based communication network
Human Immunodeficiency Virus (HIV) infection is initiated by binding of the viral glycoprotein gp120, to the cellular receptor CD4. On CD4 binding, gp120 undergoes conformational change, permitting binding to the chemokine receptor. Crystal structures of gp120 ternary complex reveal the CD4 bound conformation of gp120. We report here the application of the Gaussian network model (GNM) to the crystal structures of gp120 bound to CD4 or CD4 mimic and 17b, to study the collective motions of the gp120 core and determine the communication propensities of the residue network. The GNM fluctuation profiles identify residues in the inner domain and outer domain that may facilitate conformational change or stability, respectively. Communication propensities delineate a residue network that is topologically suited for signal propagation from the Phe43 cavity throughout the gp120 outer domain. These results provide a new context for interpreting gp120 core envelope structure–function relationships. Proteins 2010. © 2010 Wiley‐Liss, Inc.
6657635
Modeling allosteric signal propagation using protein structure networks
Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains.
1264782
Signal Propagation in Proteins and Relation to Equilibrium Fluctuations
Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models.
8085459
Coarse-grained normal mode analysis in structural biology.
The realization that experimentally observed functional motions of proteins can be predicted by coarse-grained normal mode analysis has renewed interest in applications to structural biology. Notable applications include the prediction of biologically relevant motions of proteins and supramolecular structures driven by their structure-encoded collective dynamics; the refinement of low-resolution structures, including those determined by cryo-electron microscopy; and the identification of conserved dynamic patterns and mechanically key regions within protein families. Additionally, hybrid methods that couple atomic simulations with deformations derived from coarse-grained normal mode analysis are able to sample collective motions beyond the range of conventional molecular dynamics simulations. Such applications have provided great insight into the underlying principles linking protein structures to their dynamics and their dynamics to their functions.
17019150
Low-frequency normal modes that describe allosteric transitions in biological nanomachines are robust to sequence variations
By representing the high-resolution crystal structures of a number of enzymes using the elastic network model, it has been shown that only a few low-frequency normal modes are needed to describe the large-scale domain movements that are triggered by ligand binding. Here we explore a link between the nearly invariant nature of the modes that describe functional dynamics at the mesoscopic level and the large evolutionary sequence variations at the residue level. By using a structural perturbation method (SPM), which probes the residue-specific response to perturbations (or mutations), we identify a sparse network of strongly conserved residues that transmit allosteric signals in three structurally unrelated biological nanomachines, namely, DNA polymerase, myosin motor, and the Escherichia coli chaperonin. Based on the response of every mode to perturbations, which are generated by interchanging specific sequence pairs in a multiple sequence alignment, we show that the functionally relevant low-frequency modes are most robust to sequence variations. Our work shows that robustness of dynamical modes at the mesoscopic level is encoded in the structure through a sparse network of residues that transmit allosteric signals.
6007809
Allosteric transitions in the chaperonin GroEL are captured by a dominant normal mode that is most robust to sequence variations.
The Escherichia coli chaperonin GroEL, which helps proteins to fold, consists of two heptameric rings stacked back-to-back. During the reaction cycle GroEL undergoes a series of allosteric transitions triggered by ligand (substrate protein, ATP, and the cochaperonin GroES) binding. Based on an elastic network model of the bullet-shaped double-ring chaperonin GroEL-(ADP)(7)-GroES structure (R''T state), we perform a normal mode analysis to explore the energetically favorable collective motions encoded in the R''T structure. By comparing each normal mode with the observed conformational changes in the R''T --> TR'' transition, a single dominant normal mode provides a simple description of this highly intricate allosteric transition. A detailed analysis of this relatively high-frequency mode describes the structural and dynamic changes that underlie the positive intra-ring and negative inter-ring cooperativity. The dynamics embedded in the dominant mode entails highly concerted structural motions with approximate preservation of sevenfold symmetry within each ring and negatively correlated ones between the two rings. The dominant normal mode (in comparison with the other modes) is robust to parametric perturbations caused by sequence variations, which validates its functional importance. Response of the dominant mode to local changes that mimic mutations using the structural perturbation method technique leads to a wiring diagram that identifies a network of key residues that regulate the allosteric transitions. Many of these residues are located in intersubunit interfaces, and may therefore play a critical role in transmitting allosteric signals between subunits.
37248566
Allostery wiring diagrams in the transitions that drive the GroEL reaction cycle.
Determining the network of residues that transmit allosteric signals is crucial to understanding the function of biological nanomachines. During the course of a reaction cycle, biological machines in general, and Escherichia coli chaperonin GroEL in particular, undergo large-scale conformational changes in response to ligand binding. Normal mode analyses, based on structure-based coarse-grained models where each residue is represented by an alpha carbon atom, have been widely used to describe the motions encoded in the structures of proteins. Here, we propose a new Calpha-side chain elastic network model of proteins that includes information about the physical identity of each residue and accurately accounts for the side-chain topology and packing within the structure. Using the Calpha-side chain elastic network model and the structural perturbation method, which probes the response of a local perturbation at a given site at all other sites in the structure, we determine the network of key residues (allostery wiring diagram) responsible for the T-->R and R''-->T transitions in GroEL. A number of residues, both within a subunit and at the interface of two adjacent subunits, are found to be at the origin of the positive cooperativity in the ATP-driven T-->R transition. Of particular note are residues G244, R58, D83, E209, and K327. Of these, R38, D83, and K327 are highly conserved. G244 is located in the apical domain at the interface between two subunits; E209 and K327 are located in the apical domain, toward the center of a subunit; R58 and D83 are equatorial domain residues. The allostery wiring diagram shows that the network of residues are interspersed throughout the structure. Residues D83, V174, E191, and D359 play a critical role in the R''-->T transition, which implies that mutations of these residues would compromise the ATPase activity. D83 and E191 are also highly conserved; D359 is moderately conserved. The negative cooperativity between the rings in the R''-->T transition is orchestrated through several interface residues within a single ring, including N10, E434, D435, and E451. Signal from the trans ring that is transmitted across the interface between the equatorial domains is responsible for the R''-->T transition. The cochaperonin GroES plays a passive role in the R''-->T transition. Remarkably, the binding affinity of GroES for GroEL is allosterically linked to GroEL residues 350-365 that span helices K and L. The movements of helices K and L alter the polarity of the cavity throughout the GroEL functional cycle and undergo large-scale motions that are anticorrelated with the other apical domain residues. The allostery wiring diagrams for the T-->R and R''-->T transitions of GroEL provide a microscopic foundation for the cooperativity (anticooperativity) within (between) the ring (rings). Using statistical coupling analysis, we extract evolutionarily linked clusters of residues in GroEL and GroES. We find that several substrate protein binding residues as well as sites related to ATPase activity belong to a single functional network in GroEL. For GroES, the mobile loop residues and GroES/GroES interface residues are linked.
6977517
Perturbation-Response Scanning Reveals Ligand Entry-Exit Mechanisms of Ferric Binding Protein
We study apo and holo forms of the bacterial ferric binding protein (FBP) which exhibits the so-called ferric transport dilemma: it uptakes iron from the host with remarkable affinity, yet releases it with ease in the cytoplasm for subsequent use. The observations fit the “conformational selection” model whereby the existence of a weakly populated, higher energy conformation that is stabilized in the presence of the ligand is proposed. We introduce a new tool that we term perturbation-response scanning (PRS) for the analysis of remote control strategies utilized. The approach relies on the systematic use of computational perturbation/response techniques based on linear response theory, by sequentially applying directed forces on single-residues along the chain and recording the resulting relative changes in the residue coordinates. We further obtain closed-form expressions for the magnitude and the directionality of the response. Using PRS, we study the ligand release mechanisms of FBP and support the findings by molecular dynamics simulations. We find that the residue-by-residue displacements between the apo and the holo forms, as determined from the X-ray structures, are faithfully reproduced by perturbations applied on the majority of the residues of the apo form. However, once the stabilizing ligand (Fe) is integrated to the system in holo FBP, perturbing only a few select residues successfully reproduces the experimental displacements. Thus, iron uptake by FBP is a favored process in the fluctuating environment of the protein, whereas iron release is controlled by mechanisms including chelation and allostery. The directional analysis that we implement in the PRS methodology implicates the latter mechanism by leading to a few distant, charged, and exposed loop residues. Upon perturbing these, irrespective of the direction of the operating forces, we find that the cap residues involved in iron release are made to operate coherently, facilitating release of the ion.
1549512
Solvated dissipative electro-elastic network model of hydrated proteins.
Elastic network models coarse grain proteins into a network of residue beads connected by springs. We add dissipative dynamics to this mechanical system by applying overdamped Langevin equations of motion to normal-mode vibrations of the network. In addition, the network is made heterogeneous and softened at the protein surface by accounting for hydration of the ionized residues. Solvation changes the network Hessian in two ways. Diagonal solvation terms soften the spring constants and off-diagonal dipole-dipole terms correlate displacements of the ionized residues. The model is used to formulate the response functions of the electrostatic potential and electric field appearing in theories of redox reactions and spectroscopy. We also formulate the dielectric response of the protein and find that solvation of the surface ionized residues leads to a slow relaxation peak in the dielectric loss spectrum, about two orders of magnitude slower than the main peak of protein relaxation. Finally, the solvated network is used to formulate the allosteric response of the protein to ion binding. The global thermodynamics of ion binding is not strongly affected by the network solvation, but it dramatically enhances conformational changes in response to placing a charge at the active site of the protein.
1874705
Allostery in the ferredoxin protein motif does not involve a conformational switch
Regulation of protein function via cracking, or local unfolding and refolding of substructures, is becoming a widely recognized mechanism of functional control. Oftentimes, cracking events are localized to secondary and tertiary structure interactions between domains that control the optimal position for catalysis and/or the formation of protein complexes. Small changes in free energy associated with ligand binding, phosphorylation, etc., can tip the balance and provide a regulatory functional switch. However, understanding the factors controlling function in single-domain proteins is still a significant challenge to structural biologists. We investigated the functional landscape of a single-domain plant-type ferredoxin protein and the effect of a distal loop on the electron-transfer center. We find the global stability and structure are minimally perturbed with mutation, whereas the functional properties are altered. Specifically, truncating the L1,2 loop does not lead to large-scale changes in the structure, determined via X-ray crystallography. Further, the overall thermal stability of the protein is only marginally perturbed by the mutation. However, even though the mutation is distal to the iron–sulfur cluster (∼20 Å), it leads to a significant change in the redox potential of the iron–sulfur cluster (57 mV). Structure-based all-atom simulations indicate correlated dynamical changes between the surface-exposed loop and the iron–sulfur cluster-binding region. Our results suggest intrinsic communication channels within the ferredoxin fold, composed of many short-range interactions, lead to the propagation of long-range signals. Accordingly, protein interface interactions that involve L1,2 could potentially signal functional changes in distal regions, similar to what is observed in other allosteric systems.
8152729
Modeling Signal Propagation Mechanisms and Ligand-Based Conformational Dynamics of the Hsp90 Molecular Chaperone Full-Length Dimer
Hsp90 is a molecular chaperone essential for protein folding and activation in normal homeostasis and stress response. ATP binding and hydrolysis facilitate Hsp90 conformational changes required for client activation. Hsp90 plays an important role in disease states, particularly in cancer, where chaperoning of the mutated and overexpressed oncoproteins is important for function. Recent studies have illuminated mechanisms related to the chaperone function. However, an atomic resolution view of Hsp90 conformational dynamics, determined by the presence of different binding partners, is critical to define communication pathways between remote residues in different domains intimately affecting the chaperone cycle. Here, we present a computational analysis of signal propagation and long-range communication pathways in Hsp90. We carried out molecular dynamics simulations of the full-length Hsp90 dimer, combined with essential dynamics, correlation analysis, and a signal propagation model. All-atom MD simulations with timescales of 70 ns have been performed for complexes with the natural substrates ATP and ADP and for the unliganded dimer. We elucidate the mechanisms of signal propagation and determine “hot spots” involved in interdomain communication pathways from the nucleotide-binding site to the C-terminal domain interface. A comprehensive computational analysis of the Hsp90 communication pathways and dynamics at atomic resolution has revealed the role of the nucleotide in effecting conformational changes, elucidating the mechanisms of signal propagation. Functionally important residues and secondary structure elements emerge as effective mediators of communication between the nucleotide-binding site and the C-terminal interface. Furthermore, we show that specific interdomain signal propagation pathways may be activated as a function of the ligand. Our results support a “conformational selection model” of the Hsp90 mechanism, whereby the protein may exist in a dynamic equilibrium between different conformational states available on the energy landscape and binding of a specific partner can bias the equilibrium toward functionally relevant complexes.
379546
Computational Modeling of Allosteric Communication Reveals Organizing Principles of Mutation-Induced Signaling in ABL and EGFR Kinases
The emerging structural information about allosteric kinase complexes and the growing number of allosteric inhibitors call for a systematic strategy to delineate and classify mechanisms of allosteric regulation and long-range communication that control kinase activity. In this work, we have investigated mechanistic aspects of long-range communications in ABL and EGFR kinases based on the results of multiscale simulations of regulatory complexes and computational modeling of signal propagation in proteins. These approaches have been systematically employed to elucidate organizing molecular principles of allosteric signaling in the ABL and EGFR multi-domain regulatory complexes and analyze allosteric signatures of the gate-keeper cancer mutations. We have presented evidence that mechanisms of allosteric activation may have universally evolved in the ABL and EGFR regulatory complexes as a product of a functional cross-talk between the organizing αF-helix and conformationally adaptive αI-helix and αC-helix. These structural elements form a dynamic network of efficiently communicated clusters that may control the long-range interdomain coupling and allosteric activation. The results of this study have unveiled a unifying effect of the gate-keeper cancer mutations as catalysts of kinase activation, leading to the enhanced long-range communication among allosterically coupled segments and stabilization of the active kinase form. The results of this study can reconcile recent experimental studies of allosteric inhibition and long-range cooperativity between binding sites in protein kinases. The presented study offers a novel molecular insight into mechanistic aspects of allosteric kinase signaling and provides a quantitative picture of activation mechanisms in protein kinases at the atomic level.
40856635
The signaling pathway of rhodopsin.
The signal-transduction mechanism of rhodopsin was studied by molecular dynamics (MD) simulations of the high-resolution, inactive structure in an explicit membrane environment. The simulations were employed to calculate equal-time correlations of the fluctuating interaction energy of residue pairs. The resulting interaction-correlation matrix was used to determine a network that couples retinal to the cytoplasmic interface, where transducin binds. Two highly conserved motifs, D(E)RY and NPxxY, were found to have strong interaction correlation with retinal. MD simulations with restraints on each transmembrane helix indicated that the major signal-transduction pathway involves the interdigitating side chains of helices VI and VII. The functional roles of specific residues were elucidated by the calculated effect of retinal isomerization from 11-cis to all-trans on the residue-residue interaction pattern. It is suggested that Glu134 may act as a "signal amplifier" and that Asp83 may introduce a threshold to prevent background noise from activating rhodopsin.
22938393
Signaling pathways of PDZ2 domain: A molecular dynamics interaction correlation analysis
PDZ domains are found in many signaling proteins. One of their functions is to provide scaffolds for forming membrane‐associated protein complexes by binding to the carboxyl termini of their partners. PDZ domains are thought also to play a signal transduction role by propagating the information that binding has occurred to remote sites. In this study, a molecular dynamics (MD) simulation‐based approach, referred to as an interaction correlation analysis, is applied to the PDZ2 domain to identify the possible signal transduction pathways. A residue correlation matrix is constructed from the interaction energy correlations between all residue pairs obtained from the MD simulations. Two continuous interaction pathways, starting at the ligand binding pocket, are identified by a hierarchical clustering analysis of the residue correlation matrix. One pathway is mainly localized at the N‐terminal side of helix α1 and the adjacent C‐terminus of loop β1‐β2. The other pathway is perpendicular to the central β‐sheet and extends toward the side of PDZ2 domain opposite to the ligand binding pocket. The results complement previous studies based on multiple sequence analysis, NMR, and MD simulations. Importantly, they reveal the energetic origin of the long‐range coupling. The PDZ2 results, as well as the earlier rhodopsin analysis, show that the interaction correlation analysis is a robust approach for determining pathways of intramolecular signal transduction. Proteins 2009. © 2008 Wiley‐Liss, Inc.
31528794
Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles.
Allostery describes altered protein function at one site due to a perturbation at another site. One mechanism of allostery involves correlated motions, which can occur even in the absence of substantial conformational change. We present a novel method, "MutInf", to identify statistically significant correlated motions from equilibrium molecular dynamics simulations. Our approach analyzes both backbone and sidechain motions using internal coordinates to account for the gear-like twists that can take place even in the absence of the large conformational changes typical of traditional allosteric proteins. We quantify correlated motions using a mutual information metric, which we extend to incorporate data from multiple short simulations and to filter out correlations that are not statistically significant. Applying our approach to uncover mechanisms of cooperative small molecule binding in human interleukin-2, we identify clusters of correlated residues from 50 ns of molecular dynamics simulations. Interestingly, two of the clusters with the strongest correlations highlight known cooperative small-molecule binding sites and show substantial correlations between these sites. These cooperative binding sites on interleukin-2 are correlated not only through the hydrophobic core of the protein but also through a dynamic polar network of hydrogen bonding and electrostatic interactions. Since this approach identifies correlated conformations in an unbiased, statistically robust manner, it should be a useful tool for finding novel or "orphan" allosteric sites in proteins of biological and therapeutic importance.
21455283
Binding sites in Escherichia coli dihydrofolate reductase communicate by modulating the conformational ensemble.
To explore how distal mutations affect binding sites and how binding sites in proteins communicate, an ensemble-based model of the native state was used to define the energetic connectivities between the different structural elements of Escherichia coli dihydrofolate reductase. Analysis of this model protein has allowed us to identify two important aspects of intramolecular communication. First, within a protein, pair-wise couplings exist that define the magnitude and extent to which mutational effects propagate from the point of origin. These pair-wise couplings can be identified from a quantity we define as the residue-specific connectivity. Second, in addition to the pair-wise energetic coupling between residues, there exists functional connectivity, which identifies energetic coupling between entire functional elements (i.e., binding sites) and the rest of the protein. Analysis of the energetic couplings provides access to the thermodynamic domain structure in dihydrofolate reductase as well as the susceptibility of the different regions of the protein to both small-scale (e.g., point mutations) and large-scale perturbations (e. g., binding ligand). The results point toward a view of allosterism and signal transduction wherein perturbations do not necessarily propagate through structure via a series of conformational distortions that extend from one active site to another. Instead, the observed behavior is a manifestation of the distribution of states in the ensemble and how the distribution is affected by the perturbation.
21547469
Ensemble‐based signatures of energy propagation in proteins: A new view of an old phenomenon
The ability of a protein to transmit the energetic effects of binding from one site to another constitutes the underlying basis for allosterism and signal transduction. Despite clear experimental evidence indicating the ability of proteins to transmit the effects of binding, the means by which this propagation is facilitated is not well understood. Using our previously developed ensemble‐based description of the equilibrium, we investigated the physical basis of energy propagation and identified several fundamental and general aspects of energetic coupling between residues in a protein. First, partitioning of a conformational ensemble into four distinct sub‐ensembles allows for explanation of the range of experimentally observed coupling behaviors (i.e., positive, neutral, and negative coupling between various regions of the protein structure). Second, the relative thermodynamic properties of these four sub‐ensembles define the energetic coupling between residues as either positive, neutral, or negative. Third, analysis of the structural and thermodynamic features of the states within each sub‐ensemble reveals significant variability. This third result suggests that a quantitative description of energy propagation in proteins requires an understanding of the structural and energetic features of more than just one or a few low‐energy states, but also of many high‐energy states. Such findings illuminate the difficulty in interpreting energy propagation in proteins in terms of a structural pathway that physically links coupled sites. Proteins 2006. © 2005 Wiley‐Liss, Inc.
23769807
Functional residues serve a dominant role in mediating the cooperativity of the protein ensemble
Conformational fluctuations in proteins have emerged as a potentially important aspect of biological function, although the precise relationship and the implications have yet to be fully explored. Numerous studies have reported that the binding of ligand can influence fluctuations. However, the role of the binding site in mediating these fluctuations is not known. Of particular interest is whether in addition to serving as structural scaffolds for recognition and catalysis, active-site residues may also play a role in modulating the cooperative network. To address this question, we employ an experimentally validated ensemble-based description of proteins to elucidate the extent to which perturbations at different sites can influence the cooperative network in the protein. Applying this method to a database of test proteins, it is found statistically that binding sites are located in regions most able to affect the cooperative network, even for cooperative interactions between residues distant to the binding sites. This indicates that the conformational manifold under native conditions is determined by the network of cooperative interactions within the protein and suggests that proteins have evolved to use these conformational fluctuations in carrying out their functions. Furthermore, because the energetic coupling pattern calculated for each protein is robust and relatively insensitive to sequence, these studies further suggest that binding sites evolved in regions of the protein that are inherently poised to take advantage of the fluctuations in the native structure.
14160580
Rational modulation of conformational fluctuations in adenylate kinase reveals a local unfolding mechanism for allostery and functional adaptation in proteins
Elucidating the complex interplay between protein structure and dynamics is a prerequisite to an understanding of both function and adaptation in proteins. Unfortunately, it has been difficult to experimentally decouple these effects because it is challenging to rationally design mutations that will either affect the structure but not the dynamics, or that will affect the dynamics but not the structure. Here we adopt a mutation approach that is based on a thermal adaptation strategy observed in nature, and we use it to study the binding interaction of Escherichia coli adenylate kinase (AK). We rationally design several single-site, surface-exposed glycine mutations to selectively perturb the excited state conformational repertoire, leaving the ground-state X-ray crystallographic structure unaffected. The results not only demonstrate that the conformational ensemble of AK is significantly populated by a locally unfolded state that is depopulated upon binding, but also that the excited-state conformational ensemble can be manipulated through mutation, independent of perturbations of the ground-state structures. The implications of these results are twofold. First, they indicate that it is possible to rationally design dynamic allosteric mutations, which do not propagate through a pathway of structural distortions connecting the mutated and the functional sites. Secondly and equally as important, the results reveal a general strategy for thermal adaptation that allows enzymes to modulate binding affinity by controlling the amount of local unfolding in the native-state ensemble. These findings open new avenues for rational protein design and fundamentally illuminate the role of local unfolding in function and adaptation.
5419506
Perturbation waves in proteins and protein networks: applications of percolation and game theories in signaling and drug design.
The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein molecules and in protein-protein interaction networks (interactomes). We assess the possibilities and critically review the initial attempts for the application of game theory to the often rather complicated process, when two protein molecules approach each other, mutually adjust their conformations via multiple communication steps and finally, bind to each other. We also summarize available data on the application of percolation theory for the prediction of amino acid network- and interactome-dynamics. Furthermore, we give an overview of the dissection of signals and noise in the cellular context of various perturbations. Finally, we propose possible applications of the reviewed methodologies in drug design.
2703997
Energy flow in proteins.
Energy flows anisotropically through the residues and vibrational states of globular proteins. A variety of experimental and computational studies have identified energy transport channels traversing many residues, in some cases connecting functional regions, potentially important in allostery, and in other cases having no apparent function. This property and the diffusion of energy in proteins are mimicked by transport on a percolation cluster. I review work that addresses connections between globular proteins, percolation clusters, and the similarity of energy flow and thermal transport in these systems. I also review experimental and theoretical studies of the anisotropic flow of energy through the vibrational states of a protein, a property that also can be understood by comparison with simple model disordered systems.
16595425
Residue network in protein native structure belongs to the universality class of a three-dimensional critical percolation cluster.
Single protein molecules are regarded as contact networks of amino-acid residues. Relationships between the shortest path lengths and the numbers of residues within single molecules in the native structures are examined for various sized proteins. A universal scaling among proteins is obtained, which shows that the residue networks are fractal networks. This universal fractal network is characterized with three kinds of dimensions: the network topological dimension D{c} approximately 1.9 , the fractal dimension D{f} approximately 2.5 , and the spectral dimension D{s} approximately 1.3 . These values are in surprisingly good coincidence with those of the three-dimensional critical percolation cluster. Hence the residue contact networks in the protein native structures belong to the universality class of the three-dimensional percolation cluster. The criticality is relevant to the ambivalence in the protein native structures, the coexistence of stability and instability, both of which are necessary for protein functions.
27363485
Frequency-resolved communication maps for proteins and other nanoscale materials.
Proteins exhibit highly anisotropic flow of energy. There is presently much interest in locating channels that enable signaling between distant sites, possibly playing a role in protein function such as allostery. We present an approach for computing frequency-resolved local energy diffusivities, which provide a map of communication between protein residues as a function of the vibrational frequencies of the modes that carry energy between them. A network of vibrational energy transport channels can then be identified at select frequencies. Over most frequency ranges the communication maps vary widely with vibrational frequency, as we illustrate for the protein myoglobin. We expect the approach presented here to be useful in mapping frequency-sensitive signaling in a variety of materials on the nanoscale.
53487313
Energy transfer in nonlinear network models of proteins
We investigate how nonlinearity and topological disorder affect the energy relaxation of local kicks in coarse-grained network models of proteins. We find that nonlinearity promotes long-range, coherent transfer of substantial energy to specific functional sites, while depressing transfer to generic locations. In some cases, transfer is mediated by the self-localization of discrete breathers at distant locations from the kick, acting as efficient energy-accumulating centers.
17193044
Long-range energy transfer in proteins.
Proteins are large and complex molecular machines. In order to perform their function, most of them need energy, e.g. either in the form of a photon, as in the case of the visual pigment rhodopsin, or through the breaking of a chemical bond, as in the presence of adenosine triphosphate (ATP). Such energy, in turn, has to be transmitted to specific locations, often several tens of A away from where it is initially released. Here we show, within the framework of a coarse-grained nonlinear network model, that energy in a protein can jump from site to site with high yields, covering in many instances remarkably large distances. Following single-site excitations, few specific sites are targeted, systematically within the stiffest regions. Such energy transfers mark the spontaneous formation of a localized mode of nonlinear origin at the destination site, which acts as an efficient energy-accumulating center. Interestingly, yields are found to be optimum for excitation energies in the range of biologically relevant ones.
21252202
Relationships between ligand binding sites, protein architecture and correlated paths of energy and conformational fluctuations.
The statistical thermodynamics basis of energy and residue position fluctuations is explained for native proteins. The protein and its surroundings are treated as a canonical system with emphasis on the effects of energy exchange between the two. Fluctuations of the energy are related to fluctuations of residue positions, which in turn are related to the connectivity matrix of the protein, thus establishing a connection between energy fluctuation pathways and protein architecture. The model gives the locations of hotspots for ligand binding and identifies the pathways of energy conduction within the protein. Results are discussed in terms of two sets of models, the BPTI and 12 proteins that contain the PDZ domain. A possible use of the model for determining functionally similar domains in a diverse set of proteins is pointed out.
59436343
Mapping the intramolecular vibrational energy flow in proteins reveals functionally important residues
Unveiling the mechanisms of energy relaxation in biomolecules is key to our understanding of protein stability, allostery, intramolecular signaling, and long-lasting quantum coherence phenomena at ambient temperatures. Yet, the relationship between the pathways of energy transfer and the functional role of the residues involved remains largely unknown. Here, we develop a simulation method of mapping out residues that are highly efficient in relaxing an initially localized excess vibrational energy and perform site-directed mutagenesis functional assays to assess the relevance of these residues to protein function. We use the ligand binding domains of thyroid hormone receptor (TR) subtypes as a test case and find that conserved arginines, which are critical to TR transactivation function, are the most effective heat diffusers across the protein structure. These results suggest a hitherto unsuspected connection between a residue’s ability to mediate intramolecular vibrational energy redistribution and its f...
32198198
Computational studies of LXR molecular interactions reveal an allosteric communication pathway
The liver X receptor, LXRα, is an important regulator of genes involved in metabolism and inflammation. The mechanism of communication between the cofactor peptide and the ligand in the ligand‐binding pocket is a crucial and often discussed issue for the nuclear receptors (NRs), but such allosteric signaling pathways are difficult to detect and the transmission mechanism remains elusive. Here, we apply the anisotropic thermal diffusion method to the LXRα with bound coactivator and ligand. We detected a possible communication pathway between the coactivator peptide and the ligand. The signal is transmitted both through the receptor backbone and side chains. A key signaling residue is the first leucine in the cofactor peptide recognition motif LXXLL, which is conserved within the NR cofactors, suggesting a general mechanism for allosteric signaling. Furthermore, we studied the LXR receptor and cofactor molecular interactions in detail using molecular dynamics simulations. The protein–protein interaction patterns in the complexes of nine different cofactor peptides and holo‐LXRα were characterized, revealing the importance of the receptor–cofactor charge clamp interaction. Specific, but infrequently occurring interactions were observed toward the cofactor peptide C‐terminal residues. Thus, additional specificity between LXRα and its cofactors is likely to be found in molecular interactions outside the cofactor peptide or in other biological factors. Proteins 2012;. ©2011 Wiley Periodicals, Inc.
28448168
Pump‐probe molecular dynamics as a tool for studying protein motion and long range coupling
A new method for analyzing the dynamics of proteins is developed and tested. The method, pump‐probe molecular dynamics, excites selected atoms or residues with a set of oscillating forces, and the transmission of the impulse to other parts of the protein is probed using Fourier transform of the atomic motions. From this analysis, a coupling profile can be determined which quantifies the degree of interaction between pump and probe residues. Various physical properties of the method such as reciprocity and speed of transmission are examined to establish the soundness of the method. The coupling strength can be used to address questions such as the degree of interaction between different residues at the level of dynamics, and identify propagation of influence of one part of the protein on another via “pathways” through the protein. The method is illustrated by analysis of coupling between different secondary structure elements in the allosteric protein calmodulin, and by analysis of pathways of residue–residue interaction in the PDZ domain protein previously elucidated by genomics and mutational studies. Proteins 2006. © 2006 Wiley‐Liss, Inc.
17825450
A New Concept to Reveal Protein Dynamics Based on Energy Dissipation
Protein dynamics is essential for its function, especially for intramolecular signal transduction. In this work we propose a new concept, energy dissipation model, to systematically reveal protein dynamics upon effector binding and energy perturbation. The concept is applied to better understand the intramolecular signal transduction during allostery of enzymes. The E. coli allosteric enzyme, aspartokinase III, is used as a model system and special molecular dynamics simulations are designed and carried out. Computational results indicate that the number of residues affected by external energy perturbation (i.e. caused by a ligand binding) during the energy dissipation process shows a sigmoid pattern. Using the two-state Boltzmann equation, we define two parameters, the half response time and the dissipation rate constant, which can be used to well characterize the energy dissipation process. For the allostery of aspartokinase III, the residue response time indicates that besides the ACT2 signal transduction pathway, there is another pathway between the regulatory site and the catalytic site, which is suggested to be the β15-αK loop of ACT1. We further introduce the term “protein dynamical modules” based on the residue response time. Different from the protein structural modules which merely provide information about the structural stability of proteins, protein dynamical modules could reveal protein characteristics from the perspective of dynamics. Finally, the energy dissipation model is applied to investigate E. coli aspartokinase III mutations to better understand the desensitization of product feedback inhibition via allostery. In conclusion, the new concept proposed in this paper gives a novel holistic view of protein dynamics, a key question in biology with high impacts for both biotechnology and biomedicine.
11041304
Discovery of Intramolecular Signal Transduction Network Based on a New Protein Dynamics Model of Energy Dissipation
A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.
94103871
Energy transfer pathways relevant for long-range intramolecular signaling of photosensory protein revealed by microscopic energy conductivity analysis
We report a theoretical/computational analysis of the energy flow relevant to the long-range intramolecular crosstalk between different regions in a photosensory receptor, photoactive yellow protein (PYP). To analyze the energy flow in atomic detail, we derived a theoretical expression for the interresidue energy conductivity in terms of the time-correlation function of the interatomic energy flux. The values of energy conductivities were numerically evaluated by using a long molecular dynamics simulation trajectory of the PYP molecule in the aqueous solution environment. As a result, we detected several pathways for energy transfer relevant for the long-range intramolecular signalling of PYP.
10509027
Localizing frustration in native proteins and protein assemblies
We propose a method of quantifying the degree of frustration manifested by spatially local interactions in protein biomolecules. This method of localization smoothly generalizes the global criterion for an energy landscape to be funneled to the native state, which is in keeping with the principle of minimal frustration. A survey of the structural database shows that natural proteins are multiply connected by a web of local interactions that are individually minimally frustrated. In contrast, highly frustrated interactions are found clustered on the surface, often near binding sites. These binding sites become less frustrated upon complex formation.
29952818
On the role of frustration in the energy landscapes of allosteric proteins
Natural protein domains must be sufficiently stable to fold but often need to be locally unstable to function. Overall, strong energetic conflicts are minimized in native states satisfying the principle of minimal frustration. Local violations of this principle open up possibilities to form the complex multifunnel energy landscapes needed for large-scale conformational changes. We survey the local frustration patterns of allosteric domains and show that the regions that reconfigure are often enriched in patches of highly frustrated interactions, consistent both with the idea that these locally frustrated regions may act as specific hinges or that proteins may “crack” in these locations. On the other hand, the symmetry of multimeric protein assemblies allows near degeneracy by reconfiguring while maintaining minimally frustrated interactions. We also anecdotally examine some specific examples of complex conformational changes and speculate on the role of frustration in the kinetics of allosteric change.
3051784
Rigidity and flexibility of biological networks
The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.
4998275
Induced fit, conformational selection and independent dynamic segments: an extended view of binding events.
Single molecule and NMR measurements of protein dynamics increasingly uncover the complexity of binding scenarios. Here, we describe an extended conformational selection model that embraces a repertoire of selection and adjustment processes. Induced fit can be viewed as a subset of this repertoire, whose contribution is affected by the bond types stabilizing the interaction and the differences between the interacting partners. We argue that protein segments whose dynamics are distinct from the rest of the protein ('discrete breathers') can govern conformational transitions and allosteric propagation that accompany binding processes and, as such, might be more sensitive to mutational events. Additionally, we highlight the dynamic complexity of binding scenarios as they relate to events such as aggregation and signalling, and the crowded cellular environment.
8846757
Protein flexibility predictions using graph theory
Techniques from graph theory are applied to analyze the bond networks in proteins and identify the flexible and rigid regions. The bond network consists of distance constraints defined by the covalent and hydrogen bonds and salt bridges in the protein, identified by geometric and energetic criteria. We use an algorithm that counts the degrees of freedom within this constraint network and that identifies all the rigid and flexible substructures in the protein, including overconstrained regions (with more crosslinking bonds than are needed to rigidify the region) and underconstrained or flexible regions, in which dihedral bond rotations can occur. The number of extra constraints or remaining degrees of bond‐rotational freedom within a substructure quantifies its relative rigidity/flexibility and provides a flexibility index for each bond in the structure. This novel computational procedure, first used in the analysis of glassy materials, is approximately a million times faster than molecular dynamics simulations and captures the essential conformational flexibility of the protein main and side‐chains from analysis of a single, static three‐dimensional structure. This approach is demonstrated by comparison with experimental measures of flexibility for three proteins in which hinge and loop motion are essential for biological function: HIV protease, adenylate kinase, and dihydrofolate reductase. Proteins 2001;44:150–165. © 2001 Wiley‐Liss, Inc.
6181826
Network rigidity at finite temperature: relationships between thermodynamic stability, the nonadditivity of entropy, and cooperativity in molecular systems.
A statistical mechanical distance constraint model (DCM) is presented that explicitly accounts for network rigidity among constraints present within a system. Constraints are characterized by local microscopic free-energy functions. Topological rearrangements of thermally fluctuating constraints are permitted. The partition function is obtained by combining microscopic free energies of individual constraints using network rigidity as an underlying long-range mechanical interaction, giving a quantitative explanation for the nonadditivity in component entropies exhibited in molecular systems. Two exactly solved two-dimensional toy models representing flexible molecules that can undergo conformational change are presented to elucidate concepts, and to outline a DCM calculation scheme applicable to many types of physical systems. It is proposed that network rigidity plays a central role in balancing the energetic and entropic contributions to the free energy of biopolymers, such as proteins. As a demonstration, the distance constraint model is solved exactly for the alpha-helix to coil transition in homogeneous peptides. Temperature and size independent model parameters are fitted to Monte Carlo simulation data, which includes peptides of length 10 for gas phase, and lengths 10, 15, 20, and 30 in water. The DCM is compared to the Lifson-Roig model. It is found that network rigidity provides a mechanism for cooperativity in molecular structures including their ability to spontaneously self-organize. In particular, the formation of a characteristic topological arrangement of constraints is associated with the most probable microstates changing under different thermodynamic conditions.
46558690
Correlating allostery with rigidity.
Allosteric proteins demonstrate the phenomenon of a ligand binding to a protein at a regulatory or effector site and thereby changing the chemical affinity of the catalytic site. As such, allostery is extremely important biologically as a regulatory mechanism for molecular concentrations in many cellular processes. One particularly interesting feature of allostery is that often the catalytic and effector sites are separated by a large distance. Structural comparisons of allosteric proteins resolved in both inactive and active states indicate that a variety of structural rearrangement and changes in motions may contribute to general allosteric behavior. In general it is expected that the coupling of catalytic and regulatory sites is responsible for allosteric behavior. We utilize a novel examination of allostery using rigidity analysis of the underlying graph of the protein structures. Our results indicate a general global change in rigidity associated with allosteric transitions where the R state is more rigid than the T state. A set of allosteric proteins with heterotropic interactions is used to test the hypothesis that catalytic and effector sites are structurally coupled. Observation of a rigid path connecting the effector and catalytic sites in 68.75% of the structures points to rigidity as a means by which the distal sites communicate with each other and so contribute to allosteric regulation. Thus structural rigidity is shown to be a fundamental underlying property that promotes cooperativity and non-locality seen in allostery.
15043026
Probing Molecular Mechanisms of the Hsp90 Chaperone: Biophysical Modeling Identifies Key Regulators of Functional Dynamics
Deciphering functional mechanisms of the Hsp90 chaperone machinery is an important objective in cancer biology aiming to facilitate discovery of targeted anti-cancer therapies. Despite significant advances in understanding structure and function of molecular chaperones, organizing molecular principles that control the relationship between conformational diversity and functional mechanisms of the Hsp90 activity lack a sufficient quantitative characterization. We combined molecular dynamics simulations, principal component analysis, the energy landscape model and structure-functional analysis of Hsp90 regulatory interactions to systematically investigate functional dynamics of the molecular chaperone. This approach has identified a network of conserved regions common to the Hsp90 chaperones that could play a universal role in coordinating functional dynamics, principal collective motions and allosteric signaling of Hsp90. We have found that these functional motifs may be utilized by the molecular chaperone machinery to act collectively as central regulators of Hsp90 dynamics and activity, including the inter-domain communications, control of ATP hydrolysis, and protein client binding. These findings have provided support to a long-standing assertion that allosteric regulation and catalysis may have emerged via common evolutionary routes. The interaction networks regulating functional motions of Hsp90 may be determined by the inherent structural architecture of the molecular chaperone. At the same time, the thermodynamics-based “conformational selection” of functional states is likely to be activated based on the nature of the binding partner. This mechanistic model of Hsp90 dynamics and function is consistent with the notion that allosteric networks orchestrating cooperative protein motions can be formed by evolutionary conserved and sparsely connected residue clusters. Hence, allosteric signaling through a small network of distantly connected residue clusters may be a rather general functional requirement encoded across molecular chaperones. The obtained insights may be useful in guiding discovery of allosteric Hsp90 inhibitors targeting protein interfaces with co-chaperones and protein binding clients.
35546649
Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery.
Allostery is a common mechanism of controlling many biological processes such as enzyme catalysis, signal transduction, and metabolic regulation. The use of allostery to regulate protein activity is an important and promising strategy in drug discovery and biological network regulation. In order to modulate protein activity by allostery, predictive methods need to be developed to discover allosteric binding sites. In the present study, we developed a new approach to identify allosteric sites in proteins based on the coarse-grained two-state Go̅ model. Starting from the concept that allostery is a conformation population shift process, we first constructed an ensemble of two functional states of a protein and tuned the energy landscape to bias one state. We then added perturbations to a binding site and monitored the population distribution of the new ensemble. If population redistribution occurred, then the binding perturbed site was predicted as a potential allosteric site. Our approach successfully identified all the known allosteric sites in a set of test proteins. Several new allosteric sites in the test proteins were also predicted. By use of one of the new allosteric sites predicted from Escherichia coli phosphoglycerate dehydrogenase (PGDH), novel allosteric regulating molecules were screened by molecular docking and enzymatic assay. Three novel allosteric inhibitors were discovered and their binding modes were confirmed by mutation experiments and competitive assay. The IC50 of the strongest inhibitor discovered was 21 μM, which is comparable to that of the native allosteric inhibitor l-serine. The novel allosteric site discovered in PGDH is l-serine-independent, and inhibitors targeting this site can be used as novel regulators of the E. coli serine synthesis pathway. Our approach for allosteric site prediction is generally applicable and the predicted sites can be used in discovering novel allosteric regulating molecules.
10754834
Structure-Based Predictive Models for Allosteric Hot Spots
In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues.
26146545
Mapping of two networks of residues that exhibit structural and dynamical changes upon binding in a PDZ domain protein.
We describe the changes in structure and dynamics that occur in the second PDZ domain of human tyrosine phosphatase 1E upon binding the small peptide RA-GEF2 by an analysis of NMR data based on their use as ensemble-averaged restraints in molecular dynamics simulations. This approach reveals the presence of two interconnected networks of residues, the first exhibiting structural changes and the second dynamical changes upon binding, and it provides a detailed mapping of the regions of increased and decreased mobility upon binding. Analysis of the dynamical properties of the residues in these networks reveals that conformational changes are transmitted through pathways of coupled side-chain reorientations. These results illustrate how the strategy we described, in which NMR data are used in combination with molecular dynamics simulations, can be used to characterize in detail the complex organization of the changes in structure and dynamics that take place in proteins upon binding.
23677942
Statistical mechanics of protein allostery: roles of backbone and side-chain structural fluctuations.
A statistical mechanical model of allosteric transition of proteins is developed by extending the structure-based model of protein folding to cases that a protein has two different native conformations. Partition function is calculated exactly within the model and free-energy surfaces associated with allostery are derived. In this paper, the model of allosteric transition proposed in a previous paper [Proc. Natl. Acad. Sci. U.S.A 134, 7775 (2010)] is reformulated to describe both fluctuation in side-chain configurations and that in backbone structures in a balanced way. The model is applied to example proteins, Ras, calmodulin, and CheY: Ras undergoes the allosteric transition between guanosine diphosphate (GDP)-bound and guanosine triphosphate (GTP)-bound forms, and the model results show that the GDP-bound form is stabilized enough to prevent unnecessary signal transmission, but the conformation in the GTP-bound state bears large fluctuation in side-chain configurations, which may help to bind multiple target proteins for multiple pathways of signaling. The calculated results of calmodulin show the scenario of sequential ordering in Ca(2+) binding and the associated allosteric conformational change, which are realized though the sequential appearing of pre-existing structural fluctuations, i.e., fluctuations to show structures suitable to bind Ca(2+) before its binding. Here, the pre-existing fluctuations to accept the second and third Ca(2+) ions are dominated by the side-chain fluctuation. In CheY, the calculated side-chain fluctuation of Tyr106 is coordinated with the backbone structural change in the β4-α4 loop, which explains the pre-existing Y-T coupling process in this protein. Ability of the model to explain allosteric transitions of example proteins supports the view that the large entropic effects lower the free-energy barrier of allosteric transition.
1425973
Long-Range Intra-Protein Communication Can Be Transmitted by Correlated Side-Chain Fluctuations Alone
Allosteric regulation is a key component of cellular communication, but the way in which information is passed from one site to another within a folded protein is not often clear. While backbone motions have long been considered essential for long-range information conveyance, side-chain motions have rarely been considered. In this work, we demonstrate their potential utility using Monte Carlo sampling of side-chain torsional angles on a fixed backbone to quantify correlations amongst side-chain inter-rotameric motions. Results indicate that long-range correlations of side-chain fluctuations can arise independently from several different types of interactions: steric repulsions, implicit solvent interactions, or hydrogen bonding and salt-bridge interactions. These robust correlations persist across the entire protein (up to 60 Å in the case of calmodulin) and can propagate long-range changes in side-chain variability in response to single residue perturbations.
123006898
Do topological parameters of amino acids within protein contact networks depend on their physico-chemical properties?
The three dimensional structure of a protein is determined by the interactions of its constituent amino acids. Considering the amino acids as nodes and the non-bonded interactions among them in 3D space as edges, researchers have constructed protein contact networks and analyzed the values of several topological parameters to uncover different important aspects of proteins. Here, we have analyzed some of the topological parameters such as degree, strength, clustering coefficients, betweenness and closeness centrality of each of the twenty amino acids in a set of non-redundant proteins covering all classes and folds. The results show that the values of these topological parameters vary widely with different amino acids. Also, these values differ significantly with different length scales of proteins. Most of the hydrophobic residues along with Cys, Arg and His have larger contributions to the long range connectivities than short range. We have also studied whether the values of topological parameters have any significant dependency on the physico-chemical properties of the amino acids. While the clustering coefficients show a strong negative correlation with residual volumes, surface areas and number of atoms in the side chains of amino acids; the degrees, strengths and betweenness show positive correlations with the mentioned properties. All the topological parameters show high dependency on bulkiness and average area buried of the amino acid residues in all-range residue networks. The average degree shows higher dependency on hydrophobicity, while the average strength is more able to capture the essences of surface area, residual volume and number of atoms of amino acids. The hydrophobicities of the amino acids and their corresponding degrees show a higher positive correlation in long range networks (LRNs) than short range networks (SRNs). The closeness centrality shows high correlation with two hydrophobic scales and no correlation with surface area, residual volume or number of atoms in LRNs. We have further explored the relationship in hydrophobic, hydrophilic and charged residues separately. Interestingly, charged residues show a higher dependency on the number of atoms than their residual volumes and surface areas. Finally, we present a linear regression model relating the network parameters with physico-chemical properties of amino acids.
15177563
New amino acid indices based on residue network topology.
Amino acid indices are useful tools in bioinformatics. With the appearance of novel theory and technology, and the rapid increase of experimental data, building new indices to cope with new or unsolved old problems is still necessary. In this study, residue networks are constructed from the PDB structures of 640 representative proteins based on the distance between Calpha atoms with an 8 A cutoff. All these networks show typical small world features. New amino acid indices, termed relative connectivity, clustering coefficient, closeness and betweenness, are derived from the corresponding topological parameters of amino acids in the residue networks. The 4 new network based indices are closely clustered together and related to hydrophobicity and beta propensity. When compared with related amino acid indices, the new indices show better or comparable performance in protein surface residue prediction. Relative connectivity is the best index and can reach a useful performance with an area under the curve about 0.75. It indicates that the network property based amino acid indices can be useful complements to the existing physicochemical property based amino acid indices.
12206163
A Chemical Group Graph Representation for Efficient High-Throughput Analysis of atomistic protein Simulations
Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using this representation form a powerful event detection system with significant advantages over residue-based graph representations. We additionally compare graph edges based on probability of contact to graph edges based on contact strength and analyses of the entire graph structure to an alternative and more computationally tractable node-based analysis. We develop the simplest useful technique for analyzing protein simulations based on these comparisons and use it to shed light on the speed with which static protein structures adjust to a solvated environment at room temperature in simulation.
7857302
Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins
Transcription factors and other allosteric cell signaling proteins contain a disproportionate number of domains or segments that are intrinsically disordered (ID) under native conditions. In many cases folding of these segments is coupled to binding with one or more of their interaction partners, suggesting that intrinsic disorder plays an important functional role. Despite numerous hypotheses for the role of ID domains in regulation, a mechanistic model has yet to be established that can quantitatively assess the importance of intrinsic disorder for intramolecular site-to-site communication, the hallmark property of allosteric proteins. Here, we present such a model and show that site-to-site allosteric coupling is maximized when intrinsic disorder is present in the domains or segments containing one or both of the coupled binding sites. This result not only explains the prevalence of ID domains in regulatory proteins, it also calls into question the classical mechanical view of energy propagation in proteins, which predicts that site-to-site coupling would be maximized when a well defined pathway of folded structure connects the two sites. Furthermore, in showing that the coupling mechanism conferred by intrinsic disorder is robust and independent of the network of interactions that physically link the coupled sites, unique insights are gained into the energetic ground rules that govern site-to-site communication in all proteins.
46246842
Communication maps computed for homodimeric hemoglobin: computational study of water-mediated energy transport in proteins.
Frequency-resolved communication maps provide a coarse-grained picture of energy transport in nanoscale systems. We calculate communication maps for homodimeric hemoglobin from Scapharca inaequivalvis and sample them to elucidate energy transfer pathways between the binding sites and other parts of the protein with focus on the role of the cluster of water molecules at the interface between the globules. We complement analysis of communication maps with molecular simulations of energy flow. Both approaches reveal that excess energy in one heme flows mainly to regions of the interface where early hydrogen bond rearrangements occur in the allosteric transition. In particular, energy is carried disproportionately by the water molecules, consistent with the larger thermal conductivity of water compared to proteins.
109830983
Weak links : stabilizers of complex systems from proteins to social networks
A Principle is Born: The Granovetter Study.- Why Do We Like Networks?.- Network Stability.- Weak Links as Stabilizers of Complex Systems.- Atoms, Molecules and Macromolecules.- Weak Links and Cellular Stability.- Weak Links and the Stability of Organisms.- Social Nets.- Networks of Human Culture.- The Global Web.- The Ecoweb.- Conclusions and Perspectives.
10950726
Network biology: understanding the cell's functional organization
A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.
844120
Amplification of signaling via cellular allosteric relay and protein disorder
Historical perspectives and the underlying mechanisms of allostery have been recently reviewed (1–4), increasingly highlighting conformation selection and population shift (5). A classical example is transcription factor (TF) catabolite activator protein (CAP) activation by cAMP in response to extracellular stimuli (6). cAMP–CAP–DNA complex was the first TF with 3D structure (7), and it was postulated that cAMP binding causes structural rearrangements allowing CAP to bind DNA (6, 8). Twenty-eight years later, NMR structures have confirmed the allosteric transition paradigm; as reported in this issue of PNAS, Popovych et al. (9) found that without cAMP binding, the CAP apo form loses 3 helical turns and the DNA binding domain rotates, shifting from its DNA binding-ready state. Consistent with theoretical expectation, some mutations shifted the ensemble toward a DNA-compatible active state even without cAMP binding (9). Coiled-coil order-disorder transition coupled with cAMP binding permits efficient allo steric control; yet, it is not necessarily used in other structurally similar cAMP binding domains (10). Protein kinase A undergoes a kink transition sufficient to break the inactive protein kinase A (PKA) tetramer complex on cAMP binding (11). In the CAP/fumarate and nitrate reduction regulator (FNR) superfamily, the CooA (effector: CO) does not involve an order–disorder transition but dissimilative nitrate respiration regulator (DNR) (effector: NO) does (9).
29161692
The human Mediator complex: a versatile, genome-wide regulator of transcription.
The Mediator complex interacts extensively with the RNA polymerase II enzyme and regulates its ability to express protein-coding genes. The mechanisms by which Mediator regulates gene expression remain poorly understood, in part because the structure of Mediator and even its composition can change, depending upon the promoter context. Combined with the sheer size of the human Mediator complex (26 subunits, 1.2 MDa), this structural adaptability bestows seemingly unlimited regulatory potential within the complex. Recent efforts to understand Mediator structure and function have identified expanded roles that include control of both pre- and post-initiation events; it is also evident that Mediator performs both general and gene-specific roles to regulate gene expression.
9002100
Propagation of large concentration changes in reversible protein-binding networks
We study how the dynamic equilibrium of the reversible protein–protein-binding network in yeast Saccharomyces cerevisiae responds to large changes in abundances of individual proteins. The magnitude of shifts between free and bound concentrations of their immediate and more distant neighbors in the network is influenced by such factors as the network topology, the distribution of protein concentrations among its nodes, and the average binding strength. Our primary conclusion is that, on average, the effects of a perturbation are strongly localized and exponentially decay with the network distance away from the perturbed node, which explains why, despite globally connected topology, individual functional modules in such networks are able to operate fairly independently. We also found that under specific favorable conditions, realized in a significant number of paths in the yeast network, concentration perturbations can selectively propagate over considerable network distances (up to four steps). Such “action-at-a-distance” requires high concentrations of heterodimers along the path as well as low free (unbound) concentration of intermediate proteins.
20491040
Phosphoproteomic Analysis Reveals Interconnected System-Wide Responses to Perturbations of Kinases and Phosphatases in Yeast
A system-wide analysis of protein phosphorylation in yeast reveals robustness in the network of kinases and phosphatases. Holistic Approach Protein kinases and phosphatases make attractive targets for therapies. Although various such enzymes have been characterized individually in vitro, an understanding of their roles in vivo, in the context of the entire network of kinases and phosphatases, is lacking. Indeed, inadequate knowledge of the downstream, indirect consequences of targeting a particular enzyme has led to the discontinuation of potential therapies. Bodenmiller et al. (listen to the accompanying Podcast) individually targeted most of the kinases and phosphatases in yeast, and they performed phosphoproteomic analysis of the effects of these deletions or mutations on the cellular phosphorylation network. They found that the network was surprisingly robust to perturbations in individual enzymes and that a large number of changes occurred in phosphoproteins that were not direct substrates of the targeted kinase or phosphatase. This approach should serve as a starting point toward understanding the complexity of phosphorylation regulation in yeast and other organisms. The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery—and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.
5552551
Estimating the size of the human interactome
After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be ≈650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and ≈3 times bigger than in Caenorhabditis elegans.
8235923
A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome
Protein-protein interaction maps provide a valuable framework for a better understanding of the functional organization of the proteome. To detect interacting pairs of human proteins systematically, a protein matrix of 4456 baits and 5632 preys was screened by automated yeast two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel interactions among 1705 proteins, resulting in a large, highly connected network. Independent pull-down and co-immunoprecipitation assays validated the overall quality of the Y2H interactions. Using topological and GO criteria, a scoring system was developed to define 911 high-confidence interactions among 401 proteins. Furthermore, the network was searched for interactions linking uncharacterized gene products and human disease proteins to regulatory cellular pathways. Two novel Axin-1 interactions were validated experimentally, characterizing ANP32A and CRMP1 as modulators of Wnt signaling. Systematic human protein interaction screens can lead to a more comprehensive understanding of protein function and cellular processes.
6177990
Initial characterization of the human central proteome
BackgroundOn the basis of large proteomics datasets measured from seven human cell lines we consider their intersection as an approximation of the human central proteome, which is the set of proteins ubiquitously expressed in all human cells. Composition and properties of the central proteome are investigated through bioinformatics analyses.ResultsWe experimentally identify a central proteome comprising 1,124 proteins that are ubiquitously and abundantly expressed in human cells using state of the art mass spectrometry and protein identification bioinformatics. The main represented functions are proteostasis, primary metabolism and proliferation. We further characterize the central proteome considering gene structures, conservation, interaction networks, pathways, drug targets, and coordination of biological processes. Among other new findings, we show that the central proteome is encoded by exon-rich genes, indicating an increased regulatory flexibility through alternative splicing to adapt to multiple environments, and that the protein interaction network linking the central proteome is very efficient for synchronizing translation with other biological processes. Surprisingly, at least 10% of the central proteome has no or very limited functional annotation.ConclusionsOur data and analysis provide a new and deeper description of the human central proteome compared to previous results thereby extending and complementing our knowledge of commonly expressed human proteins. All the data are made publicly available to help other researchers who, for instance, need to compare or link focused datasets to a common background.
12477334
The Levinthal paradox of the interactome
The central biological question of the 21st century is: how does a viable cell emerge from the bewildering combinatorial complexity of its molecular components? Here, we estimate the combinatorics of self‐assembling the protein constituents of a yeast cell, a number so vast that the functional interactome could only have emerged by iterative hierarchic assembly of its component sub‐assemblies. A protein can undergo both reversible denaturation and hierarchic self‐assembly spontaneously, but a functioning interactome must expend energy to achieve viability. Consequently, it is implausible that a completely “denatured” cell could be reversibly renatured spontaneously, like a protein. Instead, new cells are generated by the division of pre‐existing cells, an unbroken chain of renewal tracking back through contingent conditions and evolving responses to the origin of life on the prebiotic earth. We surmise that this non‐deterministic temporal continuum could not be reconstructed de novo under present conditions.
35622579
Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions
The integration of molecular networks with other types of data, such as changing levels of gene expression or protein‐structural features, can provide richer information about interactions than the simple node‐and‐edge representations commonly used in the network community. For example, the mapping of 3D‐structural data onto networks enables classification of proteins into singlish‐ or multi‐interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi‐interface hubs display a greater degree of conformational change than do singlish‐interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.
14774938
Detecting Mutually Exclusive Interactions in Protein-Protein Interaction Maps
Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.
40958965
'7TM receptor allostery: putting numbers to shapeshifting proteins.
Protein allosterism is the change in protein reactivity at one site arising from a molecule binding on the protein at another site. Although allosterism traditionally has been discussed in terms of affinity changes of receptors, the increasing use of functional pharmacological assays makes it mandatory to consider effects on both the affinity and the efficacy. Antagonism of agonist response can occur allosterically by reduction of affinity and/or efficacy but the antagonist will have different properties depending on which of these is primarily affected. This paper discusses the collective behaviors of seven transmembrane (7TM) receptors as allosteric systems that have a modulator (ligand or protein) that interacts and transmits information through a conduit (receptor) to a guest (either other ligand, interacting protein or cytosolic protein). Such receptor allostery can be discussed as vectorial transfers of information from ligand-binding domains ('classical' modulator allosterism) to the cytosol (functional selectivity) and along the plane of the membrane (receptor dimerization).
6022903
Seven Transmembrane Receptors as Shapeshifting Proteins: The Impact of Allosteric Modulation and Functional Selectivity on New Drug Discovery
It is useful to consider seven transmembrane receptors (7TMRs) as disordered proteins able to allosterically respond to a number of binding partners. Considering 7TMRs as allosteric systems, affinity and efficacy can be thought of in terms of energy flow between a modulator, conduit (the receptor protein), and a number of guests. These guests can be other molecules, receptors, membrane-bound proteins, or signaling proteins in the cytosol. These vectorial flows of energy can yield standard canonical guest allostery (allosteric modification of drug effect), effects along the plane of the cell membrane (receptor oligomerization), or effects directed into the cytosol (differential signaling as functional selectivity). This review discusses these apparently diverse pharmacological effects in terms of molecular dynamics and protein ensemble theory, which tends to unify 7TMR behavior toward cells. Special consideration will be given to functional selectivity (biased agonism and biased antagonism) in terms of mechanism of action and potential therapeutic application. The explosion of technology that has enabled observation of diverse 7TMR behavior has also shown how drugs can have multiple (pluridimensional) efficacies and how this can cause paradoxical drug classification and nomenclatures.
16680317
Discovery of a Novel Chemical Class of mGlu5 Allosteric Ligands with Distinct Modes of Pharmacology
We previously discovered a positive allosteric modulator (PAM) of the metabotropic glutamate receptor subtype 5 (mGlu5) termed 4 N-{4-chloro-2-[(1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)methyl]phenyl}-2-hydroxybenzamide (CPPHA) that elicits receptor activation through a novel allosteric site on mGlu5, distinct from the classical mGlu5 negative allosteric modulator (NAM) MPEP allosteric site. However, a shallow structure−activity relationship (SAR), poor physiochemical properties, and weak PAM activity at rat mGlu5 limited the utility of CPPHA to explore allosteric activation of mGlu5 at a non-MPEP site. Thus, we performed a functional high-throughput screen (HTS) and identified a novel mGlu5 PAM benzamide scaffold, exemplified by VU0001850 (EC50 = 1.3 μM, 106% Glumax) and VU0040237 (EC50 = 350 nM, 84% Glu Max). An iterative parallel synthesis approach delivered 22 analogues, optimized mGlu5 PAM activity to afford VU0357121 (EC50 = 33 nM, 92% Glumax), and also revealed the first non-MPEP site neutral allosteric ligand (VU0365396). Like CPPHA, PAMs within this class do not appear to bind at the MPEP allosteric site based on radioligand binding studies. Moreover, mutagenesis studies indicate that VU0357121 and related analogues bind to a yet uncharacterized allosteric site on mGlu5, distinct from CPPHA, yet share a functional interaction with the MPEP site.
41374151
Conformational spread: the propagation of allosteric states in large multiprotein complexes.
The phenomenon of allostery is conventionally described for small symmetrical oligomeric proteins such as hemoglobin. Here we review experimental evidence from a variety of systems-including bacterial chemotaxis receptors, muscle ryanodine receptors, and actin filaments-showing that conformational changes can also propagate through extended lattices of protein molecules. We explore the statistical mechanics of idealized linear and two-dimensional arrays of allosteric proteins and show that, as in the analogous Ising models, arrays of closely packed units can show large-scale integrated behavior. We also discuss proteins that undergo conformational changes driven by the hydrolysis of ATP and give examples in which these changes propagate through linear chains of molecules. We suggest that conformational spread could provide the basis of a solid-state "circuitry" in a living cell, able to integrate biochemical and biophysical events over hundreds of protein molecules.
4960070
BRAF Gene Amplification Can Promote Acquired Resistance to MEK Inhibitors in Cancer Cells Harboring the BRAF V600E Mutation
Amplification of an upstream gene leads to resistance to cancer drugs targeted downstream. Making Resistance Futile One of the lures of research into signal transduction is the possibility that it could lead to the development of highly targeted therapies that specifically interfere with pathways that are perturbed under pathological conditions. For instance, the discovery that mutations in BRAF are associated with various cancers led to the investigation of pharmacological inhibitors of BRAF—or the downstream kinase MEK (mitogen-activated or extracellular signal–regulated protein kinase kinase)—as therapies for individuals with BRAF-mutant tumors. All too often, however, initial promising clinical responses to such targeted therapies are followed by relapse, as cancer cells develop resistance to a particular therapeutic agent. Corcoran et al. explored the mechanisms whereby BRAF-mutant colorectal cancer cells became resistant to a MEK inhibitor and discovered that resistance involved amplification of the mutant BRAF gene. Furthermore, they found that cancer cells resistant to inhibition of either MEK or BRAF alone remained sensitive to the combined inhibition of BRAF and MEK, undergoing apoptosis at low concentrations of the inhibitors. They thus propose that, in individuals with BRAF-mutant tumors, combined inhibition of MEK and BRAF could overcome resistance to targeted therapy—and perhaps prevent it from arising. Oncogenic BRAF mutations are found in several tumor types, including melanomas and colorectal cancers. Tumors with BRAF mutations have increased mitogen-activated protein kinase pathway activity and heightened sensitivity to BRAF and MEK (mitogen-activated or extracellular signal–regulated protein kinase kinase) inhibitors. To identify potential mechanisms of acquired drug resistance, we generated clones resistant to the allosteric MEK inhibitor AZD6244 from two BRAF V600E mutant colorectal cancer cell lines that are highly sensitive to MEK or BRAF inhibition. These AZD6244-resistant (AR) clones, which exhibited cross-resistance to BRAF inhibitors, acquired resistance through amplification of the BRAF gene. A small percentage of treatment-naïve parental cells showed preexisting BRAF amplification. We observed similar amplification in a subset of cells in a BRAF-mutant colorectal cancer. In cell lines, BRAF amplification increased the abundance of phosphorylated MEK and impaired the ability of AZD6244 to inhibit ERK (extracellular signal–regulated kinase) phosphorylation. The ability of AZD6244 to inhibit ERK phosphorylation in AR cells was restored by treatment with a BRAF inhibitor at low concentrations that reduced the abundance of phosphorylated MEK to amounts observed in parental cells. Combined MEK and BRAF inhibition fully overcame resistance to MEK or BRAF inhibitors alone and was also more effective in parental cells compared to treatment with either inhibitor alone. These findings implicate BRAF amplification as a mechanism of resistance to both MEK and BRAF inhibitors and suggest combined MEK and BRAF inhibition as a clinical strategy to overcome, or possibly prevent, this mechanism of resistance.
11064487
mTOR and cancer: many loops in one pathway.
The mammalian target of rapamycin (mTOR) is a master regulator of cell growth and division that responds to a variety of stimuli, including nutrient, energy, and growth factors. In the last years, a significant number of pieces have been added to the puzzle of how mTOR coordinates and executes its functions. Extensive research on mTOR has also uncovered a complex network of regulatory loops that impact the therapeutic approaches aimed at targeting mTOR.
22754645
Dissecting the role of mTOR: lessons from mTOR inhibitors.
Recent years have observed significant advances in our understanding of how the serine/threonine kinase target of rapamycin (TOR) controls key cellular processes such as cell survival, growth and proliferation. Consistent with its role in cell proliferation, the mTOR pathway is frequently hyperactivated in a number of human malignancies and is thus considered to be an attractive target for anti-cancer therapy. Rapamycin and its analogs (rapalogs) function as allosteric inhibitors of mTORC1 and are currently used in the treatment of advanced renal cell carcinoma. Rapamycin and its derivatives bind to the small immunophilin FKBP12 to inhibit mTORC1 signalling through a poorly understood mechanism. Rapamycin/FKBP12 efficiently inhibit some, but not all, functions of mTOR and hence much interest has been placed in the development of drugs that target the kinase activity of mTOR directly. Several novel active-site inhibitors of mTOR, which inhibit both mTORC1 and mTORC2, were developed in the last year. In this manuscript, we provide a brief outline of our current understanding of the mTOR signalling pathway and review the molecular underpinnings of the action of rapamycin and novel active-site mTOR inhibitors as well as potential advantages and caveats associated with the use of these drugs in the treatment of cancer.
207151341
Designing transient binding drugs: a new concept for drug discovery.
A multitude of weak, or transient, biological interactions (dissociation constant: K(d)>microM), either working alone or in concert, occur frequently throughout biological systems. We are starting to appreciate their importance in complex biological networks. This realization has important implications to drug discovery as we can question the current paradigm of drug design to find the highest possible binders (drugs) to a given target (receptor). Development of transient drugs, defined by their binding to target, can be based on high-off-rates, multivalent approaches or multiple targets. Now, techniques are available to discover such drug candidates. The greatest problem yet to overcome is probably the mind-set of the individual researcher that weak binders are undesired and therefore of no benefit.
205147172
Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective.
Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason for why oncogenesis recapitulates ontogenesis. The formal concept of "cancer attractors", derived from an integrative, complex systems approach to gene regulatory network may provide a natural explanation. Here we present the theory of attractors in gene network dynamics and review the concept of cell types as attractors. We argue that cancer cells are trapped in abnormal attractors and discuss this concept in the light of recent ideas in cancer biology, including cancer genomics and cancer stem cells, as well as the implications for differentiation therapy.
18553230
Diseases as network perturbations.
The tremendous amount of the data obtained from the study of complex biological systems changes our view on the pathogenesis of human diseases. Instead of looking at individual components of biological processes, we focus our attention more on the interaction and dynamics of biological systems. A network representation and analysis of the physiology and pathophysiology of biological systems is an effective way to study their complex behavior. Specific perturbations can trigger cascades of failures, which lead to the malfunctioning of cellular networks and as a result to the development of specific diseases. In this review we discuss recent developments in the field of disease network analysis and highlight some of the topics and views that we think are important for understanding network-based disease mechanisms.
6626907
The relationship between rational drug design and drug side effects
Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.
1628141
Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery
MOTIVATION
Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult.


RESULTS
We present SignaLink, a database containing eight major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster and humans. Based on 170 review and approximately 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the eight pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery.


CONCLUSIONS
We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease, and underscore its importance in network-based drug target selection.


AVAILABILITY
http://SignaLink.org.
10849056
Alternative Protein-Protein Interfaces Are Frequent Exceptions
The intricate molecular details of protein-protein interactions (PPIs) are crucial for function. Therefore, measuring the same interacting protein pair again, we expect the same result. This work measured the similarity in the molecular details of interaction for the same and for homologous protein pairs between different experiments. All scores analyzed suggested that different experiments often find exceptions in the interfaces of similar PPIs: up to 22% of all comparisons revealed some differences even for sequence-identical pairs of proteins. The corresponding number for pairs of close homologs reached 68%. Conversely, the interfaces differed entirely for 12–29% of all comparisons. All these estimates were calculated after redundancy reduction. The magnitude of interface differences ranged from subtle to the extreme, as illustrated by a few examples. An extreme case was a change of the interacting domains between two observations of the same biological interaction. One reason for different interfaces was the number of copies of an interaction in the same complex: the probability of observing alternative binding modes increases with the number of copies. Even after removing the special cases with alternative hetero-interfaces to the same homomer, a substantial variability remained. Our results strongly support the surprising notion that there are many alternative solutions to make the intricate molecular details of PPIs crucial for function.
25948178
Cellular crowding imposes global constraints on the chemistry and evolution of proteomes
In living cells, functional protein–protein interactions compete with a much larger number of nonfunctional, or promiscuous, interactions. Several cellular properties contribute to avoiding unwanted protein interactions, including regulation of gene expression, cellular compartmentalization, and high specificity and affinity of functional interactions. Here we investigate whether other mechanisms exist that shape the sequence and structure of proteins to favor their correct assembly into functional protein complexes. To examine this question, we project evolutionary and cellular abundance information onto 397, 196, and 631 proteins of known 3D structure from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, respectively. On the basis of amino acid frequencies in interface patches versus the solvent-accessible protein surface, we define a propensity or “stickiness” scale for each of the 20 amino acids. We find that the propensity to interact in a nonspecific manner is inversely correlated with abundance. In other words, high abundance proteins have less sticky surfaces. We also find that stickiness constrains protein evolution, whereby residues in sticky surface patches are more conserved than those found in nonsticky patches. Finally, we find that the constraint imposed by stickiness on protein divergence is proportional to protein abundance, which provides mechanistic insights into the correlation between protein conservation and protein abundance. Overall, the avoidance of nonfunctional interactions significantly influences the physico-chemical and evolutionary properties of proteins. Remarkably, the effects observed are consistently larger in E. coli and S. cerevisiae than in H. sapiens, suggesting that promiscuous protein–protein interactions may be freer to accumulate in the human lineage.
40083318
On the supertertiary structure of proteins.
Intrinsically disordered proteins and complex multidomain proteins are characterized by a dynamic ensemble of conformations that cannot be unequivocally described by traditional static terms of structural biology. The functional importance of this structural complexity necessitates new standards and protocols for the description and deposition of such 'supertertiary' structural ensembles into structural databases.
1017154
Interfacial inhibition of macromolecular interactions: nature's paradigm for drug discovery.
One of nature's strategies for interfering with molecular interactions is to trap macromolecules in transition states with their partners in dead-end complexes that are unable to complete their biological function. This type of inhibition, which we refer to as "interfacial inhibition", is illustrated by two natural inhibitors, brefeldin A (BFA) and camptothecin (CPT), whose modes of action have been elucidated fully in structural studies. Interfacial inhibition occurs at the protein-protein interface in the case of BFA and at the protein-DNA interface in the case of CPT. In both systems, the drugs take advantage of transient structural and energetic conditions created by the macromolecular complex, which give rise to "hot-spots" for drug binding. In addition to these examples, several natural compounds such as forskolin, tubulin inhibitors and immunophilins target protein interfaces. We propose that interfacial inhibition is a paradigm for the discovery of drugs that interfere with macromolecular complexes.
35197002
Towards Drugs Targeting Multiple Proteins in a Systems Biology Approach
Protein-protein interactions are increasingly becoming drug targets. This is understandable, since they are crucial at all levels of cellular expression and growth. In practice, targeting specific disease-related interactions has proven difficult, with success varying with specific complexes. Here, we take a Systems Biology approach to targeting protein-protein interactions. Below, we first briefly review drug discovery targeted at protein-protein interactions; we classify protein-protein complexes with respect to their types of interactions and their roles in cellular function and as being targets in drug design; we describe the properties of the interfaces as related to drug design, focusing on hot spots and surface cavities; and finally, in particular, we cast the interactions into the cellular network system, highlighting the challenge of partially targeting multiple interactions in the networks as compared to hitting a specific proteinprotein interaction target. The challenge we now face is how to pick the targets and how to improve the efficiency of designed partiallyspecific multi-target drugs that would block parallel pathways in the network.
13018967
Knitting and untying the protein network: Modulation of protein ensembles as a therapeutic strategy
Proteins constitute the working machinery and structural support of all organisms. In performing a given function, they must adopt highly specific structures that can change with their level of activity, often through the direct or indirect action of other proteins. Indeed, proteins typically function within an ensemble, rather than individually. Hence, they must be sufficiently flexible to interact with each other and execute diverse tasks. The discovery that errors within these groups can ultimately cause disease has led to a paradigm shift in drug discovery, from an emphasis on single protein targets to a holistic approach whereby entire ensembles are targeted.
206209754
Enzyme inhibition by allosteric capture of an inactive conformation.
All members of the human herpesvirus protease (HHV Pr) family are active as weakly associating dimers but inactive as monomers. A small-molecule allosteric inhibitor of Kaposi's sarcoma-associated herpesvirus protease (KSHV Pr) traps the enzyme in an inactive monomeric state where the C-terminal helices are unfolded and the hydrophobic dimer interface is exposed. NMR titration studies demonstrate that the inhibitor binds to KSHV Pr monomers with low micromolar affinity. A 2.0-Å-resolution X-ray crystal structure of a C-terminal truncated KSHV Pr-inhibitor complex locates the binding pocket at the dimer interface and displays significant conformational perturbations at the active site, 15 Å from the allosteric site. NMR and CD data suggest that the small molecule inhibits human cytomegalovirus protease via a similar mechanism. As all HHV Prs are functionally and structurally homologous, the inhibitor represents a class of compounds that may be developed into broad-spectrum therapeutics that allosterically regulate enzymatic activity by disrupting protein-protein interactions.
16470233
Allosteric modulators can restore function in an amino acid neurotransmitter receptor by slightly altering intra‐molecular communication pathways
Mutations, even if not directly in the ligand binding sites of proteins, can lead to disease. In cell surface receptors, this can happen if they uncouple conformational changes that take place upon agonist (or antagonist) binding to the extracellular domain and the intracellular response. Uncoupling can take place by disrupting a major allosteric propagation pathway between the extra‐ and intracellular domains. Here I provide a mechanistic explanation: I first describe how propagation takes place; second, what can happen in the presence of a disease‐related mutation which is distant from the binding site; and finally, how drugs may overcome this disruption and rescue function. The mutations in the glycine receptor α1 subunit (α1R271Q/L) which cause the neuromotor disorder hyperekplexia are on example of such allosteric mutations. In this issue of the BJP, Shan et al. show that normal function was restored to these mutant receptors by substitution of the segment which contained the mutated position, by a homologous one. An allosteric drug could mimic the effects of such substitution. Within this framework, I highlight the advantages of allosteric drugs and the challenges in their design.
7230388
Conformational dynamics data bank: a database for conformational dynamics of proteins and supramolecular protein assemblies
The conformational dynamics data bank (CDDB, http://www.cdyn.org) is a database that aims to provide comprehensive results on the conformational dynamics of high molecular weight proteins and protein assemblies. Analysis is performed using a recently introduced coarse-grained computational approach that is applied to the majority of structures present in the electron microscopy data bank (EMDB). Results include equilibrium thermal fluctuations and elastic strain energy distributions that identify rigid versus flexible protein domains generally, as well as those associated with specific functional transitions, and correlations in molecular motions that identify molecular regions that are highly coupled dynamically, with implications for allosteric mechanisms. A practical web-based search interface enables users to easily collect conformational dynamics data in various formats. The data bank is maintained and updated automatically to include conformational dynamics results for new structural entries as they become available in the EMDB. The CDDB complements static structural information to facilitate the investigation and interpretation of the biological function of proteins and protein assemblies essential to cell function.
24476182
PRISM: protein-protein interaction prediction by structural matching.
Prism (protein interactions by structural matching) is a system that employs a novel prediction algorithm for protein-protein interactions. It adopts a bottom-up approach that combines structure and sequence conservation in protein interfaces. The algorithm seeks possible binary interactions between proteins through structure similarity and evolutionary conservation of known interfaces. It is composed of a database containing protein interface structures derived from the Protein Data Bank (PDB) and predicted protein-protein interactions. It also provides related information about the proteins and an interactive protein interface viewer. In the current version, 3799 structurally nonredundant interfaces are used to predict the interactions among 6170 proteins. A substantial number of interactions are verified in two publicly available interaction databases (DIP and BIND). As the verified interactions demonstrate the suitability of our approach, unverified ones may point to undiscovered interactions. Prism can be accessed through a user-friendly website (http://prism.ccbb.ku.edu.tr) and it will be updated regularly as new protein structures become available in the PDB. Users may browse through the nonredundant dataset of representative interfaces on which the prediction algorithm depends, retrieve the list of structures similar to these interfaces, or see the results of interaction predictions for a particular protein. Another service provided is the interactive prediction. This is done by running the algorithm for the user input structures.
6994507
Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes
Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as “computational probes” to systematically identify protein conformers with likely biological significance.
394024
Markov Methods for Hierarchical Coarse-Graining of Large Protein Dynamics
Elastic network models (ENMs) and, in particular, the Gaussian Network Model (GNM) have been widely used in recent years to gain insights into the machinery of proteins. The extension of ENMs to supramolecular assemblies presents computational challenges, because of the difficulty in retaining atomic details in mode decomposition of large protein dynamics. Here, we present a novel approach to address this problem. We rely on the premise that, all the residues of the protein machinery (network) must communicate with each other and operate in a coordinated manner to perform their function successfully. To gain insight into the mechanism of information transfer between residues, we study a Markov model of network communication. Using the Markov chain perspective, we map the full-atom network representation into a hierarchy of ENMs of decreasing resolution, perform analysis of dominant communication (or dynamic) patterns in reduced space(s) and reconstruct the detailed models with minimal loss of information. The communication properties at different levels of the hierarchy are intrinsically defined by the network topology. This new representation has several features, including: soft clustering of the protein structure into stochastically coherent regions thus providing a useful assessment of elements serving as hubs and/or transmitters in propagating information/interaction; automatic computation of the contact matrices for ENMs at each level of the hierarchy to facilitate computation of both Gaussian and anisotropic fluctuation dynamics. We illustrate the utility of the hierarchical decomposition in providing an insightful description of the supramolecular machinery by applying the methodology to the chaperonin GroEL-GroES.
9656201
Toward Understanding Allosteric Signaling Mechanisms in the ATPase Domain of Molecular Chaperones
The ATPase cycle of the heat shock protein 70 (HSP70) is largely dependent on the ability of its nucleotide binding domain (NBD), also called ATPase domain, to undergo structural changes between its open and closed conformations. We present here a combined study of the Hsp70 NBD sequence, structure and dynamic features to identify the residues that play a crucial role in mediating the allosteric signaling properties of the ATPase domain. Specifically, we identify the residues involved in the shortest-path communications of the domain modeled as a network of nodes (residues) and links (equilibrium interactions). By comparing the calculations on both closed and open conformation of Hsp70 NBD, we identified a subset of central residues located at the interface between the two lobes of the NBD near the nucleotide binding site, which form a putative communication pathway invariant to structural changes. Two pairs of residues forming contacts at the interface in the closed conformation of the NBD are observed to no longer interact in the open conformation, suggesting that these specific interactions may play a switch role in establishing the transition of the NBD between the two functional forms. Sequence co-evolution analysis and collective dynamics analysis with elastic network model further confirm the key roles of these residues in Hsp70 NBD dynamics and functions.
7166159
Role of Hsp70 ATPase Domain Intrinsic Dynamics and Sequence Evolution in Enabling its Functional Interactions with NEFs
Catalysis of ADP-ATP exchange by nucleotide exchange factors (NEFs) is central to the activity of Hsp70 molecular chaperones. Yet, the mechanism of interaction of this family of chaperones with NEFs is not well understood in the context of the sequence evolution and structural dynamics of Hsp70 ATPase domains. We studied the interactions of Hsp70 ATPase domains with four different NEFs on the basis of the evolutionary trace and co-evolution of the ATPase domain sequence, combined with elastic network modeling of the collective dynamics of the complexes. Our study reveals a subtle balance between the intrinsic (to the ATPase domain) and specific (to interactions with NEFs) mechanisms shared by the four complexes. Two classes of key residues are distinguished in the Hsp70 ATPase domain: (i) highly conserved residues, involved in nucleotide binding, which mediate, via a global hinge-bending, the ATPase domain opening irrespective of NEF binding, and (ii) not-conserved but co-evolved and highly mobile residues, engaged in specific interactions with NEFs (e.g., N57, R258, R262, E283, D285). The observed interplay between these respective intrinsic (pre-existing, structure-encoded) and specific (co-evolved, sequence-dependent) interactions provides us with insights into the allosteric dynamics and functional evolution of the modular Hsp70 ATPase domain.
4525123
Identification of key residues for protein conformational transition using elastic network model.
Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.
1376518
Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics
Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.
14426931
Network-Based Tools for the Identification of Novel Drug Targets
Analysis of network topology and dynamics holds promise for identifying new sets of potential drug targets. In the past few years, network-based tools have become increasingly important in the identification of novel molecular targets for drug development. Systems-based approaches to predict signal transduction–related drug targets have developed into an especially promising field. Here, we summarize our studies, which indicate that modular bridges and overlaps of protein-protein interaction and signaling networks may be of key importance in future drug design. Intermodular nodes are very efficient in mediating the transmission of perturbations between signaling modules and are important in network cooperation. The analysis of stress-induced rearrangements of the yeast interactome by the ModuLand modularization algorithm indicated that components of modular overlap are key players in cellular adaptation to stress. Signaling crosstalk was much more pronounced in humans than in Caenorhabditis elegans or Drosophila melanogaster in the SignaLink (http://www.SignaLink.org) database, a uniformly curated database of eight major signaling pathways. We also showed that signaling proteins that participate in multiple pathways included multiple established drug targets and drug target candidates. Lastly, we caution that the pervasive overlap of cellular network modules implies that wider use of multitarget drugs to partially inhibit multiple individual proteins will be necessary to modify specific cellular functions, because targeting single proteins for complete disruption usually affects multiple cellular functions with little specificity for a particular process. Tools for analyzing network topology and especially network dynamics have great potential to identify alternative sets of targets for developing multitarget drugs.
14118234
Detection of the dominant direction of information flow and feedback links in densely interconnected regulatory networks
BackgroundFinding the dominant direction of flow of information in densely interconnected regulatory or signaling networks is required in many applications in computational biology and neuroscience. This is achieved by first identifying and removing links which close up feedback loops in the original network and hierarchically arranging nodes in the remaining network. In mathematical language this corresponds to a problem of making a graph acyclic by removing as few links as possible and thus altering the original graph in the least possible way. The exact solution of this problem requires enumeration of all cycles and combinations of removed links, which, as an NP-hard problem, is computationally prohibitive even for modest-size networks.ResultsWe introduce and compare two approximate numerical algorithms for solving this problem: the probabilistic one based on a simulated annealing of the hierarchical layout of the network which minimizes the number of "backward" links going from lower to higher hierarchical levels, and the deterministic, "greedy" algorithm that sequentially cuts the links that participate in the largest number of feedback cycles. We find that the annealing algorithm outperforms the deterministic one in terms of speed, memory requirement, and the actual number of removed links. To further improve a visual perception of the layout produced by the annealing algorithm, we perform an additional minimization of the length of hierarchical links while keeping the number of anti-hierarchical links at their minimum. The annealing algorithm is then tested on several examples of regulatory and signaling networks/pathways operating in human cells.ConclusionThe proposed annealing algorithm is powerful enough to performs often optimal layouts of protein networks in whole organisms, consisting of around ~104 nodes and ~105 links, while the applicability of the greedy algorithm is limited to individual pathways with ~100 vertices. The considered examples indicate that the annealing algorithm produce biologically meaningful layouts: The function of the most of the anti-hierarchical links is indeed to send a feedback signal to the upstream pathway elements. Source codes of F 90 and Matlab implementation of the two algorithms are available at http://www.cmth.bnl.gov/~maslov/programs.htm
6413576
Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture
Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome‐scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome‐scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top‐layer TFs are relatively abundant, long‐lived, and noisy compared with the core‐ and bottom‐layer TFs. Although variability in expression of top‐layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core‐ and bottom‐layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.
4066060
Extracting the abstraction pyramid from complex networks
BackgroundAt present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.ResultsWe propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the inclusion (i.e., part-of) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems.ConclusionsNetworks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at http://140.113.166.165/pyramabs.php/.
7015735
HiNO: An Approach for Inferring Hierarchical Organization from Regulatory Networks
Background Gene expression as governed by the interplay of the components of regulatory networks is indeed one of the most complex fundamental processes in biological systems. Although several methods have been published to unravel the hierarchical structure of regulatory networks, weaknesses such as the incorrect or inconsistent assignment of elements to their hierarchical levels, the incapability to cope with cyclic dependencies within the networks or the need for a manual curation to retrieve non-overlapping levels remain unsolved. Methodology/Results We developed HiNO as a significant improvement of the so-called breadth-first-search (BFS) method. While BFS is capable of determining the overall hierarchical structures from gene regulatory networks, it especially has problems solving feed-forward type of loops leading to conflicts within the level assignments. We resolved these problems by adding a recursive correction approach consisting of two steps. First each vertex is placed on the lowest level that this vertex and its regulating vertices are assigned to (downgrade procedure). Second, vertices are assigned to the next higher level (upgrade procedure) if they have successors with the same level assignment and have themselves no regulators. We evaluated HiNO by comparing it with the BFS method by applying them to the regulatory networks from Saccharomyces cerevisiae and Escherichia coli, respectively. The comparison shows clearly how conflicts in level assignment are resolved in HiNO in order to produce correct hierarchical structures even on the local levels in an automated fashion. Conclusions We showed that the resolution of conflicting assignments clearly improves the BFS-method. While we restricted our analysis to gene regulatory networks, our approach is suitable to deal with any directed hierarchical networks structure such as the interaction of microRNAs or the action of non-coding RNAs in general. Furthermore we provide a user-friendly web-interface for HiNO that enables the extraction of the hierarchical structure of any directed regulatory network. Availability HiNO is freely accessible at http://mips.helmholtz-muenchen.de/hino/.
981136
Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems
To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network — the optimal number of levels and modular partition at each level — with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.
15515156
Hierarchy Measure for Complex Networks
Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.
9342350
Key to Network Controllability
Liu et al recently proposed a minimum number of driver nodes needed to obtain full structural controllability over a directed network. Driver nodes are unmatched nodes, from which there are directed paths to all matched nodes. Their most important assertion is that a system's controllability is to a great extent encoded by the underlying network's degree distribution, $P(k_{in}, k_{out})$. Is the controllability of a network decided almost completely by the immediate neighbourhood of a node, while, even slightly distant nodes play no role at all? Motivated by the above question, in this communication, we argue that an effective understanding of controllability in directed networks can be reached using distance based measures of closeness centrality and betweenness centrality and may not require the knowledge of local connectivity measures like in-degree and out-degree.
9560414
Controlling Complex Networks with Compensatory Perturbations
The response of complex networks to perturbations is of utmost importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. A fundamental property of networks is that the perturbation of one node can affect other nodes, in a process that may cause the entire or substantial part of the system to change behavior and possibly collapse. Recent research in metabolic and food-web networks has demonstrated the concept that network damage caused by external perturbations can often be mitigated or reversed by the application of compensatory perturbations. Compensatory perturbations are constrained to be physically admissible and amenable to implementation on the network. However, the systematic identification of compensatory perturbations that conform to these constraints remains an open problem. Here, we present a method to construct compensatory perturbations that can control the fate of general networks under such constraints. Our approach accounts for the full nonlinear behavior of real complex networks and can bring the system to a desirable target state even when this state is not directly accessible. Applications to genetic networks show that compensatory perturbations are effective even when limited to a small fraction of all nodes in the network and that they are far more effective when limited to the highest-degree nodes. The approach is conceptually simple and computationally efficient, making it suitable for the rescue, control, and reprogramming of large complex networks in various domains.
14060196
Controlling edge dynamics in complex networks
Surprisingly little is known about how network dynamics might be controlled, despite extensive research into how they behave. A study of the controllability of network edge dynamics reveals that it differs from that of nodal dynamics, and that real-world networks are easier to control than their random counterparts.
4334171
Controllability of complex networks
The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.
17915314
Optimizing controllability of complex networks by minimum structural perturbations.
To drive a large, complex, networked dynamical system toward some desired state using as few external signals as possible is a fundamental issue in the emerging field of controlling complex networks. Optimal control is referred to the situation where such a network can be fully controlled using only one driving signal. We propose a general approach to optimizing the controllability of complex networks by judiciously perturbing the network structure. The principle of our perturbation method is validated theoretically and demonstrated numerically for homogeneous and heterogeneous random networks and for different types of real networks as well. The applicability of our method is discussed in terms of the relative costs of establishing links and imposing external controllers. Besides the practical usage of our approach, its implementation elucidates, interestingly, the intricate relationship between certain structural properties of the network and its controllability.
7411720
Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks
Structural controllability has been proposed as an analytical framework for making predictions regarding the control of complex networks across myriad disciplines in the physical and life sciences (Liu et al., Nature:473(7346):167–173, 2011). Although the integration of control theory and network analysis is important, we argue that the application of the structural controllability framework to most if not all real-world networks leads to the conclusion that a single control input, applied to the power dominating set, is all that is needed for structural controllability. This result is consistent with the well-known fact that controllability and its dual observability are generic properties of systems. We argue that more important than issues of structural controllability are the questions of whether a system is almost uncontrollable, whether it is almost unobservable, and whether it possesses almost pole-zero cancellations.
29756212
Allosteric regulation and catalysis emerge via a common route.
Allosteric regulation of protein function is a mechanism by which an event in one place of a protein structure causes an effect at another site, much like the behavior of a telecommunications network in which a collection of transmitters, receivers and transceivers communicate with each other across long distances. For example, ligand binding or an amino acid mutation at an allosteric site can alter enzymatic activity or binding affinity in a distal region such as the active site or a second binding site. The mechanism of this site-to-site communication is of great interest, especially since allosteric effects must be considered in drug design and protein engineering. In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed. We summarize recent experimental data and the resulting insights into allostery within proteins, and we discuss the nature of future studies and the new applications that may result from increased understanding of this regulatory mechanism.
43155141
Variations in clique and community patterns in protein structures during allosteric communication: investigation of dynamically equilibrated structures of methionyl tRNA synthetase complexes.
The allosteric concept has played a key role in understanding the biological functions of proteins. The rigidity or plasticity and the conformational population are the two important ideas invoked in explaining the allosteric effect. Although molecular insights have been gained from a large number of structures, a precise assessment of the ligand-induced conformational changes in proteins at different levels, ranging from gross topology to intricate details, remains a challenge. In this study, we have explored the conformational changes in the complexes of methionyl tRNA synthetase (MetRS) through novel network parameters such as cliques and communities, which identify the rigid regions in the protein structure networks (PSNs) constructed from the noncovalent interactions of amino acid side chains. MetRS belongs to the aminoacyl tRNA synthetase (aaRS) family that plays a crucial role in the translation of genetic code. These enzymes are modular with distinct domains from which extensive genetic, kinetic, and structural data are available, highlighting the role of interdomain communication. The network parameters evaluated here on the conformational ensembles of MetRS complexes, generated from molecular dynamics simulations, have enabled us to understand the interdomain communication in detail. Additionally, the characterization of conformational changes in terms of cliques and communities has also become possible, which had eluded conventional analyses. Furthermore, we find that most of the residues participating in cliques and communities are strikingly different from those that take part in long-range communication. The cliques and communities evaluated here for the first time on PSNs have beautifully captured the local geometries in detail within the framework of global topology. Here the allosteric effect is revealed at the residue level via identification of the important residues specific for structural rigidity and functional flexibility in MetRS. This ought to enhance our understanding of the functioning of aaRS in general.
8709276
Allosteric effects in the marginally stable von Hippel–Lindau tumor suppressor protein and allostery-based rescue mutant design
Many multifunctional tumor suppressor proteins have low stability, a property linked to cancer development. The von Hippel–Lindau tumor suppressor protein (pVHL) is one of these proteins. pVHL forms part of the E3 ubiquitin ligase complex that regulates the degradation of the hypoxia-inducible factor (HIF). Under native conditions, free pVHL is a molten globule, but it is stabilized in the E3 complex. By using molecular dynamics simulations, we observed that the interface between the two pVHL domains is the least stable region in unbound pVHL. We designed five stable mutants: one with a mutation at the interdomain interface and the others in the α- or β-domains. Experimentally, type 2B pVHL disease mutant Y98N at the HIF binding site was shown to destabilize pVHL and decrease its binding affinity to HIF. Our simulations showed that the decrease in pVHL stability and binding affinity are allosterically regulated. The mutations designed to stabilize unbound wild-type pVHL, which are away from the elongin C and HIF binding sites, successfully stabilized the Y98N pVHL–elongin C complex and lowered the binding free energy of pVHL with HIF. Our results indicated both the enthalpic and dynamic allosteric components between the elongin C and HIF binding sites in pVHL, in the α- and β-domains, respectively, mediated by the interdomain interface and linker. Drugs mimicking the allosteric effects of these mutants may rescue pVHL function in von Hippel–Lindau disease.
6020043
Protein Sectors: Evolutionary Units of Three-Dimensional Structure
Proteins display a hierarchy of structural features at primary, secondary, tertiary, and higher-order levels, an organization that guides our current understanding of their biological properties and evolutionary origins. Here, we reveal a structural organization distinct from this traditional hierarchy by statistical analysis of correlated evolution between amino acids. Applied to the S1A serine proteases, the analysis indicates a decomposition of the protein into three quasi-independent groups of correlated amino acids that we term "protein sectors." Each sector is physically connected in the tertiary structure, has a distinct functional role, and constitutes an independent mode of sequence divergence in the protein family. Functionally relevant sectors are evident in other protein families as well, suggesting that they may be general features of proteins. We propose that sectors represent a structural organization of proteins that reflects their evolutionary histories.
5621337
Identification of an allosteric signaling network within Tec family kinases.
The Tec family kinases are tyrosine kinases that function primarily in hematopoietic cells. The catalytic activity of the Tec kinases is positively influenced by the regulatory domains outside of the kinase domain. The current lack of a full-length Tec kinase structure leaves a void in our understanding of how these positive regulatory signals are transmitted to the kinase domain. Recently, a conserved structure within kinases, the 'regulatory spine', which assembles and disassembles as a kinase switches between its active and inactive states, has been identified. Here, we define the residues that comprise the regulatory spine within Tec kinases. Compared to previously characterized systems, the Tec kinases contain an extended regulatory spine that includes a conserved methionine within the C-helix and a conserved tryptophan within the Src homology 2-kinase linker of Tec kinases. This extended regulatory spine forms a conduit for transmitting the presence of the regulatory domains of Tec kinases to the catalytic domain. We further show that mutation of the gatekeeper residue at the edge of the regulatory spine stabilizes the regulatory spine, resulting in a constitutively active kinase domain. Importantly, the regulatory spine is preassembled in this gatekeeper mutant, rendering phosphorylation on the activation loop unnecessary for its activity. Moreover, we show that the disruption of the conserved electrostatic interaction between Bruton's tyrosine kinase R544 on the activation loop and Bruton's tyrosine kinase E445 on the C-helix also aids in the assembly of the regulatory spine. Thus, the extended regulatory spine is a key structure that is critical for maintaining the activity of Tec kinases.
23195948
Molecular evolution of protein conformational changes revealed by a network of evolutionarily coupled residues.
An improved understanding of protein conformational changes has broad implications for elucidating the mechanisms of various biological processes and for the design of protein engineering experiments. Understanding rearrangements of residue interactions is a key component in the challenge of describing structural transitions. Evolutionary properties of protein sequences and structures are extensively studied; however, evolution of protein motions, especially with respect to interaction rearrangements, has yet to be explored. Here, we investigated the relationship between sequence evolution and protein conformational changes and discovered that structural transitions are encoded in amino acid sequences as coevolving residue pairs. Furthermore, we found that highly coevolving residues are clustered in the flexible regions of proteins and facilitate structural transitions by forming and disrupting their interactions cooperatively. Our results provide insight into the evolution of protein conformational changes and help to identify residues important for structural transitions.
3114200
Predicting disease-associated substitution of a single amino acid by analyzing residue interactions
BackgroundThe rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.ResultsWe found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.ConclusionsThe satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.
3173377
Three-dimensional reconstruction of protein networks provides insight into human genetic disease
To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.
11945828
ASD: a comprehensive database of allosteric proteins and modulators
Allostery is the most direct, rapid and efficient way of regulating protein function, ranging from the control of metabolic mechanisms to signal-transduction pathways. However, an enormous amount of unsystematic allostery information has deterred scientists who could benefit from this field. Here, we present the AlloSteric Database (ASD), the first online database that provides a central resource for the display, search and analysis of structure, function and related annotation for allosteric molecules. Currently, ASD contains 336 allosteric proteins from 101 species and 8095 modulators in three categories (activators, inhibitors and regulators). Proteins are annotated with a detailed description of allostery, biological process and related diseases, and modulators with binding affinity, physicochemical properties and therapeutic area. Integrating the information of allosteric proteins in ASD should allow for the identification of specific allosteric sites of a given subtype among proteins of the same family that can potentially serve as ideal targets for experimental validation. In addition, modulators curated in ASD can be used to investigate potent allosteric targets for the query compound, and also help chemists to implement structure modifications for novel allosteric drug design. Therefore, ASD could be a platform and a starting point for biologists and medicinal chemists for furthering allosteric research. ASD is freely available at http://mdl.shsmu.edu.cn/ASD/.
17751380
Allostery and population shift in drug discovery.
Proteins can exist in a large number of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist; and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. Here we present an overview of dynamic conformational ensembles focusing on allosteric events in signaling. We propose that combining equilibrium fluctuation concepts with genomic screens could help drug discovery.
38212643
Small molecule probes of cellular pathways and networks.
Small molecules are important not only as therapeutics to treat disease but also as chemical tools to probe complex biological processes. The discovery of novel bioactive small molecules has largely been catalyzed by screening diverse chemical libraries for alterations in specific activities in pure proteins assays or in generating cell-based phenotypes. New approaches are needed to close the vast gap between the ability to study either single proteins or whole cellular processes. This Review focuses on the growing number of studies aimed at understanding in more detail how small molecules perturb particular signaling pathways and larger networks to yield distinct cellular phenotypes. This type of pathway-level analysis and phenotypic profiling provides valuable insight into mechanistic action of small molecules and can reveal off-target effects and improve our understanding of how proteins within a pathway regulate signaling.