Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms

• Published in: Bioinformatics Vol. 21; no. suppl-1; pp. i75 - i84
• Main Authors: Cheng, Jianlin, Baldi, Pierre
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.06.2005
• Subjects: Algorithms; Computational Biology - methods; Humans; Hydrogen Bonding; Models, Chemical; Models, Molecular; Nerve Net; Protein Conformation; Protein Structure, Secondary; Proteins - chemistry; ROC Curve; X-Ray Diffraction
• ISSN: 1367-4803; 1460-2059
• DOI: 10.1093/bioinformatics/bti1004

Motivation: Protein β-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein β-sheets, however, remains challenging because protein β-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting β-sheet topological features, such as β-strand alignments, in general have not exploited the global covariation and constraints characteristic of β-sheet architectures. Results: We propose a modular approach to the problem of predicting/assembling protein β-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 β-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 β-strands. Finally, the third step uses graph matching algorithms to predict the β-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global β-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@ics.uci.edu