Three-Stage Prediction of Protein Beta-Sheets by Neural Networks, Alignments, and Graph Algorithms
Jianlin Cheng and Pierre Baldi
Protein beta-sheets play a fundamental role in protein structure,
function, evolution, and bio-engineering. 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.
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 inter-strand
beta-residues from profile, secondary structure, and solvent
accessibility information. The second step applies dynamic programming
techniques to these probabilities to derive binding pseudo-energies 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 pseudo-energy
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
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The full dataset (BetaSheet916) used in the paper.
BetaSheet916 is splitted randomly and evenly into ten folds to perform cross-validation.
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