Genetic Algorithm Managed Peptide Mutant Screening: Optimizing Peptide Ligands for Targeted Receptor Binding

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• 作者：Matthew D. King ; Thomas Long ; Timothy Andersen ; Owen M. McDougal
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2016
• 出版时间：December 27, 2016
• 年：2016
• 卷：56
• 期：12
• 页码：2378-2387
• 全文大小：598K
• ISSN：1549-960X

文摘

This study demonstrates the utility of genetic algorithms to search exceptionally large and otherwise intractable mutant libraries for sequences with optimal binding affinities for target receptors. The Genetic Algorithm Managed Peptide Mutant Screening (GAMPMS) program was used to search an α-conotoxin (α-CTx) MII mutant library of approximately 41 billion possible peptide sequences for those exhibiting the greatest binding affinity for the α₃β₂-nicotinic acetylcholine receptor (nAChR) isoform. A series of top resulting peptide ligands with high sequence homology was obtained, with each mutant having an estimated ΔG_bind approximately double that of the potent native α-CTx MII ligand. A consensus sequence from the top GAMPMS results was subjected to more rigorous binding free energy calculations by molecular dynamics and compared to α-CTx MII and other related variants for binding with α₃β₂-nAChR. In this study, the efficiency of GAMPMS to substantially reduce the sample population size through evolutionary selection criteria to produce ligands with higher predicted binding affinity is demonstrated.