8023010
PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction
Knowledge of the detailed structure of a protein is crucial to our understanding of the biological functions of that protein. The gap between the number of solved protein structures and the number of protein sequences continues to widen rapidly in the post-genomics era due to long and expensive processes for solving structures experimentally. Computational prediction of structures from amino acid sequence has come to play a key role in narrowing the gap and has been successful in providing useful information for the biological research community. We have developed a prediction pipeline, PROSPECT-PSPP, an integration of multiple computational tools, for fully automated protein structure prediction. The pipeline consists of tools for (i) preprocessing of protein sequences, which includes signal peptide prediction, protein type prediction (membrane or soluble) and protein domain partition, (ii) secondary structure prediction, (iii) fold recognition and (iv) atomic structural model generation. The centerpiece of the pipeline is our threading-based program PROSPECT. The pipeline is implemented using SOAP (Simple Object Access Protocol), which makes it easier to share our tools and resources. The pipeline has an easy-to-use user interface and is implemented on a 64-node dual processor Linux cluster. It can be used for genome-scale protein structure prediction. The pipeline is accessible at http://csbl.bmb.uga.edu/protein_pipeline.
13379605
Protein Fold Recognition Through Application of Residual Dipolar Coupling Data
Residual dipolar coupling (RDC) represents one of the most exciting emerging NMR techniques for studying protein structures. However, solving a protein structure using RDC data alone is a highly challenging problem as it often requires that the starting structure model be close to the actual structure of a protein, for the structure calculation procedure to be effective. We report in this paper a computer program, RDC-PROSPECT, for identification of a structural homolog or analog of a target protein in PDB, which best matches the 15N-1H RDC data of the protein recorded in a single ordering medium. The identified structural homolog/analog can then be used as a starting model for RDC-based structure calculation. Since RDC-PROSPECT uses only RDC data and predicted secondary structure information, its performance is virtually independent of sequence similarity between a target protein and its structural homolog/analog, making it applicable to protein targets out of the scope of current protein threading techniques. We have tested RDC-PROSPECT on all 15N-1H RDC data (representing 33 proteins) available in the BMRB database and the literature. The program correctly identified the structural folds for approximately 80% of the target proteins, significantly better than previously reported results, and achieved an average alignment accuracy of 97.9% residues within 4-residue shift. Through a careful algorithmic design, RDC-PROSPECT is at least one order of magnitude faster than previously reported algorithms for principal alignment frame search, making our algorithm fast enough for large-scale applications.
6020472
Compression-based fixed-parameter algorithms for feedback vertex set and edge bipartization
We show that the NP-complete Feedback Vertex Set problem, which asks for the smallest set of vertices to remove from a graph to destroy all cycles, is deterministically solvable in O(c^[email protected]?m) time. Here, m denotes the number of graph edges, k denotes the size of the feedback vertex set searched for, and c is a constant. We extend this to an algorithm enumerating all solutions in O(d^[email protected]?m) time for a (larger) constant d. As a further result, we present a fixed-parameter algorithm with runtime O(2^[email protected]?m^2) for the NP-complete Edge Bipartization problem, which asks for at most k edges to remove from a graph to make it bipartite.
75279
Error Compensation in Leaf Power Problems
AbstractThe k-Leaf Power recognition problem is a particular case of graph
 power problems: For a given graph it asks whether there exists an
 unrooted tree—the k-leaf root—with leaves one-to-one labeled by
 the graph vertices and where the leaves have distance at most k iff their corresponding vertices in the graph are connected by an
 edge. Here we study "error correction" versions of k-Leaf
 Power recognition—that is, adding or deleting at most l
 edges to generate a graph that has a k-leaf root. We provide
 several NP-completeness results in this context, and we show that
 the NP-complete Closest 3-Leaf Power problem (the error
 correction version of 3-Leaf Power) is fixed-parameter
 tractable with respect to the number of edge modifications or vertex
 deletions in the given graph. Thus, we provide the seemingly first
 nontrivial positive algorithmic results in the field of error
 compensation for leaf power problems with k > 2. To this end, as a
 result of independent interest, we develop a forbidden subgraph
 characterization of graphs with 3-leaf roots.
38762001
Pattern Matching for Arc-Annotated Sequences
A study of pattern matching for arc-annotated sequences is started. An O(nm) time algorithm is given to determine whether a length m sequence with nested arc annotations is an arc-preserving subsequence of a length n sequence with nested arc annotations, called APS(NESTED,NESTED). Arc-annotated sequences and, in particular, those with nested arc structure are motivated by applications in RNA structure comparison. Our algorithm can be used to accelerate a recent fixed-parameter algorithm for LAPCS(NESTED,NESTED) and generalizes results for ordered tree inclusion problems. In particular, the presented dynamic programming methodology implies a quadratic time algorithm for an open problem posed by Vialette.
9767043
The Computational Complexity of Avoiding Forbidden Submatrices by Row Deletions
We initiate a systematic study of the ROW DELETION(B) problem on matrices: Given an input matrix A and a fixed "forbidden submatrix" B, the task is to remove a minimum number of rows from A such that no row or column permutation of B occurs as a submatrix in the resulting matrix. An application of this problem can be found, for instance, in the construction of perfect phylogenies. Establishing a strong connection to variants of the NP-complete HITTING SET problem, we describe and analyze structural properties of B that make ROW DELETION(B)NP-complete. On the positive side, the close relation with HITTING SET problems yields constant-factor polynomial-time approximation algorithms and fixed-parameter tractability results.
10069776
Automated Generation of Search Tree Algorithms for Hard Graph
Modification Problems
Abstract
We present a framework for an automated 
generation of exact search tree algorithms for NP-hard problems.
The purpose of our approach is twofold—rapid development and 
improved upper bounds. 
Many search tree algorithms for various problems
in the literature are based on complicated
case distinctions. Our approach may lead to a much 
simpler process of developing and analyzing these algorithms.
Moreover, using the sheer computing power of machines it may also 
lead to improved upper bounds on
search tree sizes (i.e., faster exact solving algorithms) in comparison
with previously developed “hand-made” search trees.
Among others, such an example is given with the NP-complete 
Cluster Editing problem (also known as Correlation
Clustering on complete unweighted graphs), which asks for the minimum
number of edge additions and deletions to create a graph which is a
disjoint union of cliques. The
hand-made search tree
for Cluster Editing
had worst-case size O(2.27k), which now is improved to 
O(1.92k) due to our new method.
(Herein, k denotes the number of edge modifications allowed.)

