Three-stage prediction of protein ß-sheets by neural networks, alignments and graph algorithms
Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA 92697, USA
*To whom correspondence should be addressed.
Motivation: Protein ß-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein ß-sheets, however, remains challenging because protein ß-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting ß-sheet topological features, such as ß-strand alignments, in general have not exploited the global covariation and constraints characteristic of ß-sheet architectures.
Results: We propose a modular approach to the problem of predicting/assembling protein ß-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand ß-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of ß-strands. Finally, the third step uses graph matching algorithms to predict the ß-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global ß-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods.
Availability: http://www.igb.uci.edu/servers/psss.html
Contact: pfbaldi{at}ics.uci.edu
Received on January 15, 2005; accepted on March 27, 2005
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