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Bayesian Models and Algorithms for Protein β-Sheet Prediction
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DOI：10.1109/TCBB.2008.140.png)
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Abstract
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Prediction of the 3D structure greatly benefits from the information related to secondary structure, solvent accessibility, and nonlocal contacts that stabilize a protein's structure. We address the problem of beta-sheet prediction defined as the prediction of beta-strand pairings, interaction types (parallel or antiparallel), and beta-residue interactions (or contact maps). We introduce a Bayesian approach for proteins with six or less beta-strands in which we model the conformational features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of beta-strand organizations. To select the optimum beta-sheet architecture, we significantly reduce the search space by heuristics that enforce the amino acid pairs with strong interaction potentials. In addition, we find the optimum pairwise alignment between beta-strands using dynamic programming in which we allow any number of gaps in an alignment to model beta-bulges more effectively. For proteins with more than six beta-strands, we first compute beta-strand pairings using the BetaPro method. Then, we compute gapped alignments of the paired beta-strands and choose the interaction types and beta-residue pairings with maximum alignment scores. We performed a 10-fold cross-validation experiment on the BetaSheet916 set and obtained significant improvements in the prediction accuracy.
Keywords:
Protein beta-sheets
open beta-sheets
beta-sheet prediction
contact map prediction
Bayesian modeling
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