Bayesian models and algorithms for protein beta-sheet prediction

Aydın, Zafer and Altunbaşak, Yücel and Erdoğan, Hakan (2009) Bayesian models and algorithms for protein beta-sheet prediction. (Accepted/In Press)

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Official URL: http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4745629


Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy.

Item Type:Article
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication
ID Code:12749
Deposited By:Hakan Erdoğan
Deposited On:18 Nov 2009 21:10
Last Modified:23 Jul 2019 16:34

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