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Predicting Protein Structure Using Parallel Genetic Algorithms.
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING
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The protein folding problem is a biochemistry Grand Challenge problem. The challenge is to reliably predict natural three-dimensional structures of polypeptides. Genetic algorithms GAs are robust, semi-optimal search techniques modeling natural evolutionary processes. Fast messy GAs fmGAs are variants of messy GAs that reduce the exponential time complexity to polynomial. This investigation evaluates the merits of parallel SGAs and fmGAs for minimizing the potential energy of a pentapeptide, Met-enkephalin. AFITs energy model is compared to a similar model in a commercial package called QUANTA. Differences between the two models are identified and resolved to enhance GAs abilities to correctly fold molecules. The steps required to unify the behavior of the two implementations is presented. The effectiveness of SGAs while minimizing the potential energy of Met-enkephalin is shown to be highly dependent on the choice of population size and mutation rate. It is also demonstrated that choosing parameters from the Schaffers proposed guidelines cause SGAs to realize near-optimal performance on this particular application. Parallel SGAs are capable of finding near-optimal conformations of Met-enkephalin. Parallel fmGAS should ultimately find better solutions in less time. The experiments performed in this investigation determine limitations of parallel SGAs and fmGAs applied to polypeptide energy minimization.
APPROVED FOR PUBLIC RELEASE