Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

The road not taken: retreat and diverge in local search for simplified protein structure prediction

Swakkhar Shatabda12*, MA Hakim Newton12, Mahmood A Rashid12, Duc Nghia Pham12 and Abdul Sattar12

Author Affiliations

1 Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia

2 Queensland Research Laboratory, National ICT of Australia

For all author emails, please log on.

BMC Bioinformatics 2013, 14(Suppl 2):S19  doi:10.1186/1471-2105-14-S2-S19

Published: 21 January 2013

Abstract

Background

Given a protein's amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy.

Results

In this paper, we present an efficient local search algorithm that deals with these two problems. During search, we select the best candidate at each iteration, but store the unexplored second best candidates in a set of elite conformations, and explore them whenever the search faces stagnation. Moreover, we propose a new non-isomorphic encoding for the protein conformations to store the conformations and to check similarity when applied with a memory based search. This new encoding helps eliminate conformations that are equivalent under rotation and translation, and thus results in better prevention of re-visitation.

Conclusion

On standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice.