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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Abstract

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
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Depositing User: Hakan Erdoğan
Date Deposited: 18 Nov 2009 21:10
Last Modified: 23 Jul 2019 16:34
URI: https://research.sabanciuniv.edu/id/eprint/12749

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