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ABSTRACT: Motivation
The roughness of energy landscapes is a major obstacle to protein structure prediction, since it forces conformational searches to spend much time struggling to escape numerous traps. Specifically, beta-sheet formation is prone to stray, since many possible combinations of hydrogen bonds are dead ends in terms of beta-sheet assembly. It has been shown that cooperative terms for backbone hydrogen bonds ease this problem by augmenting hydrogen bond patterns that are consistent with beta sheets. Here, we present a novel cooperative hydrogen-bond term that is both effective in promoting beta sheets and computationally efficient. In addition, the new term is differentiable and operates on all-atom protein models.Results
Energy optimization of poly-alanine chains under the new term led to significantly more beta-sheet content than optimization under a non-cooperative term. Furthermore, the optimized structure included very few non-native patterns.Availability
The new term is implemented within the MESHI package and is freely available at http://cs.bgu.ac.il/ approximately meshi.
SUBMITTER: Levy-Moonshine A
PROVIDER: S-EPMC3140807 | biostudies-literature | 2009 Oct
REPOSITORIES: biostudies-literature
Levy-Moonshine Ami A Amir El-Ad David el-AD Keasar Chen C
Bioinformatics (Oxford, England) 20090723 20
<h4>Motivation</h4>The roughness of energy landscapes is a major obstacle to protein structure prediction, since it forces conformational searches to spend much time struggling to escape numerous traps. Specifically, beta-sheet formation is prone to stray, since many possible combinations of hydrogen bonds are dead ends in terms of beta-sheet assembly. It has been shown that cooperative terms for backbone hydrogen bonds ease this problem by augmenting hydrogen bond patterns that are consistent w ...[more]