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Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space
Author(s) -
Yang Yuedong,
Liu Haiyan
Publication year - 2006
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20463
Subject(s) - dihedral angle , pairwise comparison , energy minimization , algorithm , minification , genetic algorithm , population , function (biology) , computer science , mathematics , mathematical optimization , chemistry , computational chemistry , artificial intelligence , molecule , biology , hydrogen bond , demography , organic chemistry , evolutionary biology , sociology
We have investigated protein conformation sampling and optimization based on the genetic algorithm and discrete main chain dihedral state model. An efficient approach combining the genetic algorithm with local minimization and with a niche technique based on the sharing function is proposed. Using two different types of potential energy functions, a Go‐type potential function and a knowledge‐based pairwise potential energy function, and a test set containing small proteins of varying sizes and secondary structure compositions, we demonstrated the importance of local minimization and population diversity in protein conformation optimization with genetic algorithms. Some general properties of the sampled conformations such as their native‐likeness and the influences of including side‐chains are discussed. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1593–1602, 2006