Random-Walk: A Stagnation Recovery Technique for Simplified Protein Structure Prediction
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Protein structure prediction is a challenging optimisation problem to the computer scientists. A large number of ex- isting (meta-)heuristic search algorithms attempt to solve the problem by exploring possible structures and finding the one with minimum free energy. However, these algo- rithms 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.
ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2012 (ACM BCB)
Copyright ACM 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Random-Walk: A Stagnation Recovery Technique for Simplified Protein Structure Prediction, ISBN 978-1-4503-1670-5, http://dx.doi.org/10.1145/2382936.2383043.
Artificial Intelligence and Image Processing not elsewhere classified