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• W2110974733 endingPage "1602" @default.
• W2110974733 startingPage "1593" @default.
• W2110974733 abstract "Abstract 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" @default.
• W2110974733 created "2016-06-24" @default.
• W2110974733 creator A5023539493 @default.
• W2110974733 creator A5036086539 @default.
• W2110974733 date "2006-07-25" @default.
• W2110974733 modified "2023-09-30" @default.
• W2110974733 title "Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space" @default.
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• W2110974733 doi "https://doi.org/10.1002/jcc.20463" @default.
• W2110974733 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16868993" @default.
• W2110974733 hasPublicationYear "2006" @default.
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