Issue No. 02 - March/April (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.140
Zafer Aydin , Georgia Institute of Technology, Atlanta
Yucel Altunbasak , Georgia Institute of Technology, Atlanta
Hakan Erdogan , Sabanci University, Istanbul
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.
Protein \beta-sheets, open \beta-sheets, \beta-sheet prediction, contact map prediction, Bayesian modeling.
H. Erdogan, Z. Aydin and Y. Altunbasak, "Bayesian Models and Algorithms for Protein β-Sheet Prediction," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 395-409, 2008.