Email updates

Keep up to date with the latest news and content from Proteome Science and BioMed Central.

This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Proteome Science

Open Access Proceedings

Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface

Brian S Olson1 and Amarda Shehu123*

Author Affiliations

1 Department of Computer Science, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

2 Department of Bioinformatics and Computational Biology, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

3 Department of Bioengineering, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

For all author emails, please log on.

Proteome Science 2012, 10(Suppl 1):S5  doi:10.1186/1477-5956-10-S1-S5

Published: 21 June 2012

Abstract

Background

Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface.

Methods

This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima.

Results and conclusions

The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework.