Understanding the dynamics of ligand-protein interactions is indispensable in the design of novel therapeutic agents. In this paper, we establish the use of Stochastic Roadmap Simulation (SRS) for the study of ligand-protein interactions through two studies. In our first study, we measure the effects of mutations on the catalytic site of a protein, a process called computational mutagenesis. In our second study, we focus on distinguishing the catalytic site from other putative binding sites. SRS compactly represents many Monte Carlo (MC) simulation paths in a compact graph structure, or roadmap. Furthermore, SRS allows us to analyze all the paths in this roadmap simultaneously. In our application of SRS to the domain of ligand-protein interactions, we consider a new parameter called escape time, the expected number of MC simulation steps required for the ligand to escape from the ”funnel of attraction” of the binding site, as a metric for analyzing such interactions. Although computing escape times would probably be infeasible with MC simulation, these computations can be performed very efficiently with SRS. Our results for six mutant complexes for the first study and seven ligand-protein complexes for the second study, are very promising: In particular, the first results agree well with the biological interpretation of the mutations, while the second results show that escape time is a good metric to distinguish the catalytic site for five out of seven complexes. Understanding ligand-protein interactions is of fundamental importance. These interactions play a central role in biological processes essential to life. Studying these interactions is also an indispensable step in discovering new therapeutic molecules during the drug design process. Traditionally ligand-protein interactions are studied through laboratory experiments, which are often time consuming and costly. With the rapid advances in molecular simulation techniques and computer hardware, computational methods have become increasingly more important in these studies. They complement the laboratory methods by providing fast and inexpensive initial analyses to guide further examination through laboratory experiments. In this paper, we study the binding affinity between ligands and proteins with a recently developed approach for analyzing molecular motion, called Stochastic Roadmap Simulation (SRS), a method for representing many MC simulations simultaneously in a compact graph structure [ABG 02]. Here, we introduce the notion of escape time as a measure of binding affinity. Intuitively escape time is the expected amount of time for a ligand to escape from the ”funnel of attraction” at the binding site of a protein. Let us consider the conformation space of a ligand-protein complex with a suitably defined energy function. The binding site represents a small region of this space. If the ligand is bound with high affinity, it should take much longer to escape from this region. We hypothesize that a longer escape time is a result of high energy barriers around the catalytic site, which are likely attributable to the energy distribution at the catalytic site. We thus examine the ligand-protein binding process by directly accounting for the motion of the ligand. In contrast, most previous computational approaches analyzing ligandprotein interactions, such as [MGH 98], employ static models and consider only the final bound conformation of the ligand, and they cannot be used to compute properties of the binding process. In principle, escape time can be computed with standard simulation techniques. However, escape time is not a property of one molecular motion pathway, but an average property of many pathways. To estimate the escape time, we must calculate the time for a ligand to escape along many different pathways and then take the average. Classic simulation techniques such as the Monte Carlo (MC) and molecular dynamics methods are computationally intensive and impractical for this purpose on a workstation. The reason is that they focus on a single pathway at a time and are easily trapped in the local minima of the energy landscape. By combining SRS, which considers many pathways simultaneously and compactly, with tools from Markov chain theory, we can compute the escape time efficiently and achieve several orders of magnitude reduction in running time, compared with MC simulation. To validate our approach, we conducted two studies and obtained very promising results: In the first study, we examined the effects of mutations at the catalytic site of a protein by performing computational mutagenesis. Specifically we mutated the residues near the catalytic site of lactate dehydrogenase and observed the effects
['Mehmet Serkan Apaydin', 'Carlos Guestrin', 'Chris Varma', 'Douglas L. Brutlag', 'Jean-Claude Latombe']
Stochastic Roadmap Simulation for The Study of Ligand-Protein Interactions