Memory-Based Local Search for Simplified Protein Structure Prediction
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Protein structure prediction is one of the most challenging problems in computational biology. Given a protein's amino acid sequence, a simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. In this paper, we present a memory-based local search method for the simplified problem using Hydrophobic-Polar energy model and Face Centered Cubic lattice. By memorizing local minima and then avoiding their neighbohood, our approach significantly improves the state-of-the-art local search method for protein structure prediction on a set of standard benchmark proteins.
ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2012 (ACM BCB)
© 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 Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine , ISBN: 978-1-4503-1670-5, dx.doi.org/10.1145/2382936.2382980
Artificial Intelligence and Image Processing not elsewhere classified