Random-walk: a stagnation recovery technique for simplified protein structure prediction

Authors:
Mahmood A. Rashid;Swakkhar Shatabda;M. A. Hakim Newton;Md Tamjidul Hoque;Duc Nghia Pham;Abdul Sattar
Affiliations:
Queensland Research Laboratory, NICTA and Griffith University;Queensland Research Laboratory, NICTA and Griffith University;Queensland Research Laboratory, NICTA and Griffith University;University of New Orleans;Queensland Research Laboratory, NICTA and Griffith University;Queensland Research Laboratory, NICTA and Griffith University
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

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Abstract

Protein structure prediction is a challenging optimisation problem to the computer scientists. A large number of existing (meta-)heuristic search algorithms attempt to solve the problem by exploring possible structures and finding the one with minimum free energy. However, these algorithms often get stuck in local minima and thus perform poorly on large sized proteins. In this paper, we present a random-walk based stagnation recovery approach. We tested our approach on tabu-based local search as well as population based genetic algorithms. The experimental results show that, random-walk is very effective for escaping from local minima for protein structure prediction on face-centred-cubic lattice and hydrophobic-polar energy model.