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大规模对接的实用指南。

A practical guide to large-scale docking.

机构信息

Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA.

Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.

出版信息

Nat Protoc. 2021 Oct;16(10):4799-4832. doi: 10.1038/s41596-021-00597-z. Epub 2021 Sep 24.

Abstract

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

摘要

基于结构的大型化合物库对接筛选在药物和探针的早期发现中已变得很常见。随着计算机效率的提高和化合物库的增长,对数亿甚至数十亿化合物进行筛选的能力对于中等规模的计算机集群来说已经成为可能。这使得人们能够快速且经济有效地探索和分类广阔的化学空间,并将其划分为一个子集,其中包含针对给定靶标的潜在命中化合物。为了实现这一目标,需要使用近似值,这会导致可能的构象采样不足和绝对结合能预测不准确。因此,与其他领域一样,建立控制措施非常重要,这有助于提高尽管存在这些挑战,但仍能成功的可能性。在这里,我们概述了最佳实践和对照对接计算,以帮助在进行大规模前瞻性筛选之前评估给定靶标对接参数,我们在一个特定的靶标——褪黑素受体中举例说明,遵循该程序后,可直接获得具有亚纳摩尔范围内活性的对接命中化合物。建议增加额外的对照实验以确保经过实验验证的命中化合物具有特定的活性。无论使用哪种对接软件,这些指南都应该是有用的。本文中所描述的对接软件(DOCK3.7)免费提供给学术研究,以探索针对一系列靶标的新的命中化合物。

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