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通过挖掘最小值对蛋白质-配体结合进行建模。

Modeling Protein-Ligand Binding by Mining Minima.

作者信息

Chen Wei, Gilson Michael K, Webb Simon P, Potter Michael J

机构信息

VeraChem LLC, Germantown, MD.

出版信息

J Chem Theory Comput. 2010 Nov 9;6(11):3540-3557. doi: 10.1021/ct100245n. Epub 2010 Oct 8.

DOI:10.1021/ct100245n
PMID:22639555
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3359898/
Abstract

We present the first application of the mining minima algorithm to protein-small molecule binding. This end-point approach use an empirical force field and implicit solvent models, treats the protein binding-site as fully flexible and estimates free energies as sums over local energy wells. The calculations are found to yield encouraging agreement with experiment for three sets of HIV-1protease inhibitors and a set of phosphodiesterase 10a inhibitors. The contributions of various aspects of the model to its accuracy are examined, and the Poisson-Boltzmann correction is found to be the most critical. Interestingly, the computed changes in configurational entropy upon binding fall roughly along the same entropy-energy correlation previously observed for smaller host-guest systems. Strengths and weaknesses of the method are discussed, as are the prospects for enhancing accuracy and speed.

摘要

我们展示了挖掘最小值算法在蛋白质-小分子结合中的首次应用。这种端点方法使用经验力场和隐式溶剂模型,将蛋白质结合位点视为完全灵活的,并将自由能估计为局部能量阱的总和。计算结果表明,对于三组HIV-1蛋白酶抑制剂和一组磷酸二酯酶10a抑制剂,计算结果与实验结果令人鼓舞地吻合。研究了模型各个方面对其准确性的贡献,发现泊松-玻尔兹曼校正最为关键。有趣的是,计算得到的结合时构型熵的变化大致遵循先前在较小的主客体系统中观察到的相同熵-能量相关性。讨论了该方法的优缺点,以及提高准确性和速度的前景。

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