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...

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/∼meshi.

Contact: chen.keasar@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.