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GPCR-Bench:G蛋白偶联受体对接的基准测试集及从业者指南

GPCR-Bench: A Benchmarking Set and Practitioners' Guide for G Protein-Coupled Receptor Docking.

作者信息

Weiss Dahlia R, Bortolato Andrea, Tehan Benjamin, Mason Jonathan S

机构信息

Heptares Therapeutics Ltd. , BioPark, Broadwater Road, Welwyn Garden City, Herts, AL7 3AX, U.K.

出版信息

J Chem Inf Model. 2016 Apr 25;56(4):642-51. doi: 10.1021/acs.jcim.5b00660. Epub 2016 Mar 24.

Abstract

Virtual screening is routinely used to discover new ligands and in particular new ligand chemotypes for G protein-coupled receptors (GPCRs). To prepare for a virtual screen, we often tailor a docking protocol that will enable us to select the best candidates for further screening. To aid this, we created GPCR-Bench, a publically available docking benchmarking set in the spirit of the DUD and DUD-E reference data sets for validation studies, containing 25 nonredundant high-resolution GPCR costructures with an accompanying set of diverse ligands and computational decoy molecules for each target. Benchmarking sets are often used to compare docking protocols; however, it is important to evaluate docking methods not by "retrospective" hit rates but by the actual likelihood that they will produce novel prospective hits. Therefore, docking protocols must not only rank active molecules highly but also produce good poses that a chemist will select for purchase and screening. Currently, no simple objective machine-scriptable function exists that can do this; instead, docking hit lists must be subjectively examined in a consistent way to compare between docking methods. We present here a case study highlighting considerations we feel are of importance when evaluating a method, intended to be useful as a practitioners' guide.

摘要

虚拟筛选常用于发现新的配体,尤其是针对G蛋白偶联受体(GPCRs)的新配体化学类型。为了准备虚拟筛选,我们经常定制一种对接方案,以便能够选择最佳候选物进行进一步筛选。为此,我们创建了GPCR-Bench,这是一个公开可用的对接基准集,其设计理念源于用于验证研究的DUD和DUD-E参考数据集,包含25个非冗余的高分辨率GPCR共结构,以及针对每个靶点的一组多样的配体和计算诱饵分子。基准集通常用于比较对接方案;然而,重要的是评估对接方法不能仅依据“回顾性”命中率,而应依据它们产生新的前瞻性命中物的实际可能性。因此,对接方案不仅必须将活性分子排在高位,还必须产生化学家会选择用于购买和筛选的良好构象。目前,不存在能够做到这一点的简单客观的机器可编写脚本的函数;相反,对接命中列表必须以一致的方式进行主观检查,以便在对接方法之间进行比较。我们在此呈现一个案例研究,突出了我们认为在评估一种方法时重要的考虑因素,旨在作为从业者的指南。

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