King Matthew D, Long Thomas, Andersen Timothy, McDougal Owen M
Department of Chemistry and Biochemistry and ‡Department of Computer Science, Boise State University , 1910 University Drive, Boise, Idaho 83725, United States.
J Chem Inf Model. 2016 Dec 27;56(12):2378-2387. doi: 10.1021/acs.jcim.6b00095. Epub 2016 Dec 7.
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 αβ-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 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 αβ-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.
本研究证明了遗传算法在搜索异常庞大且原本难以处理的突变体文库以寻找与靶受体具有最佳结合亲和力的序列方面的实用性。遗传算法管理的肽突变体筛选（GAMPMS）程序被用于在一个约有410亿个可能肽序列的α-芋螺毒素（α-CTx）MII突变体文库中搜索对αβ-烟碱型乙酰胆碱受体（nAChR）亚型具有最大结合亲和力的序列。获得了一系列具有高度序列同源性的顶级肽配体，每个突变体的估计ΔG约为强效天然α-CTx MII配体的两倍。通过分子动力学对GAMPMS顶级结果的共有序列进行了更严格的结合自由能计算，并与α-CTx MII和其他相关变体与αβ-nAChR的结合情况进行了比较。在本研究中，证明了GAMPMS通过进化选择标准大幅减少样本群体规模以产生具有更高预测结合亲和力的配体的效率。
