Competitive Template Analysis of the Fast Messy Genetic Algorithm When Applied to the Protein Structure Prediction Problem

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The Protein Structure Prediction (PSP) problem is a Grand Challenge problem among biochemists, computer scientists and engineers alike. Solving this problem involves correctly predicting the geometrical conformation of a fully folded protein. This paper focuses on CHARMm energy minimization and the use of a genetic algorithm, the fast messy genetic algorithm (fmGA), to obtain solutions to this optimization problem. The fmGA is a novel algorithm that explicitly manipulates building blocks (BBs) in order to obtain “good” solutions to an optimization problem. In order to obtain these “good” solutions, fully speci¯ed competitive templates are used within the fmGA to evaluate the BBs found. This paper presents “good” results of an analysis of various competitive template schemes for the application of the fmGA to the PSP of [Met]-Enkephelin and the much larger Polyalanine peptide.

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Journal: TechConnect Briefs
Volume: 2, Technical Proceedings of the 2002 International Conference on Computational Nanoscience and Nanotechnology
Published: April 22, 2002
Pages: 36 - 39
Industry sector: Medical & Biotech
Topics: Biomaterials, Informatics, Modeling & Simulation
ISBN: 0-9708275-6-3