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基于分子动力学的监督学习(SuMD)作为一种有用的工具,可在纳秒时间尺度上描绘 GPCR-配体识别途径。

Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale.

机构信息

Molecular Modeling Section (MMS), Dipartimento di Scienze del Farmaco, Università di Padova , via Marzolo 5, 35131 Padova, Italy.

出版信息

J Chem Inf Model. 2014 Feb 24;54(2):372-6. doi: 10.1021/ci400766b. Epub 2014 Feb 3.

Abstract

Supervised MD (SuMD) is a computational method that allows the exploration of ligand-receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand-receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR-ligand complex embedded into explicit lipid-water environment.

摘要

有监督 MD(SuMD)是一种计算方法,可在纳秒(ns)时间尺度内探索配体-受体识别途径的研究。它由在配体-受体接近距离上将类似于禁忌的监督算法纳入经典分子动力学(MD)模拟技术组成。除了加快配体-受体轨迹的获取外,这种实现还通过利用嵌入到明确定义的脂质-水环境中的 GPCR-配体复合物的全原子 MD 模拟的准确性,方便了对多个结合事件(如元结合、变构和正位结合)的特征描述。

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