title: Three-stage prediction of protein beta-sheets by neural networks, alignments and graph algorithms.
abstract: BACKGROUND
  Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures.
  
  
  RESULTS
  We propose a modular approach to the problem of predicting/assembling protein beta-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 beta-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 beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods.
  
  
  BACKGROUND
  http://www.igb.uci.edu/servers/psss.html.
authors.affiliation: Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA 92697, USA.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D006860:Hydrogen Bonding; D008956:Models, Chemical; D008958:Models, Molecular; D009415:Nerve Net; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D012372:ROC Curve; D014961:X-Ray Diffraction
chemical_list: D011506:Proteins