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.
Original language | English |
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Pages (from-to) | 2639-2645 |
Number of pages | 7 |
Journal | Bioinformatics |
Volume | 25 |
Issue number | 20 |
DOIs | |
State | Published - 20 Oct 2009 |
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics