Toward Effective Polypeptide Structure Prediction with Parallel Fast Messy Genetic Algorithms

Publisher Summary The protein folding problem (PFP) involves the determination of the 3D structures of proteins given only their amino acid sequences. One reason for the significant interest in general and efficient techniques to solve this type of problem is that such knowledge would facilitate understanding of the tremendous amount of genetic information produced by the Human Genome Project. This chapter refers to efforts aimed at identifying in vivo structures of naturally occurring proteins or structures of arbitrary polypeptides in arbitrary environments. In particular, it looks at the general problem of identifying three-dimensional structures of arbitrary polypeptides in arbitrary environments, and focuses on a particular kind of evolutionary algorithm (the messy genetic algorithm) for use on this task. It concentrates on improved effectiveness through the exploitation of domain constraints (i.e., dihedral-angle constraints inspired by the Ramachandran plot) and the exploitation of prior secondary structure analysis. The work described is performed on high-grade parallel and heterogeneous computing resources.

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