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

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.