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评分函数的比较评估:CASF-2016 更新。

Comparative Assessment of Scoring Functions: The CASF-2016 Update.

机构信息

State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis , Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences , 345 Lingling Road , Shanghai 200032 , People's Republic of China.

University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China.

出版信息

J Chem Inf Model. 2019 Feb 25;59(2):895-913. doi: 10.1021/acs.jcim.8b00545. Epub 2018 Dec 11.


DOI:10.1021/acs.jcim.8b00545
PMID:30481020
Abstract

In structure-based drug design, scoring functions are often employed to evaluate protein-ligand interactions. A variety of scoring functions have been developed so far, and thus, some objective benchmarks are desired for assessing their strength and weakness. The comparative assessment of scoring functions (CASF) benchmark developed by us provides an answer to this demand. CASF is designed as a "scoring benchmark", where the scoring process is decoupled from the docking process to depict the performance of scoring function more precisely. Here, we describe the latest update of this benchmark, i.e., CASF-2016. Each scoring function is still evaluated by four metrics, including "scoring power", "ranking power", "docking power", and "screening power". Nevertheless, the evaluation methods have been improved considerably in several aspects. A new test set is compiled, which consists of 285 protein-ligand complexes with high-quality crystal structures and reliable binding constants. A panel of 25 scoring functions are tested on CASF-2016 as a demonstration. Our results reveal that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power. Scoring power is somewhat correlated with ranking power, so are docking power and screening power. The results obtained on CASF-2016 may provide valuable guidance for the end users to make smart choices among available scoring functions. Moreover, CASF is created as an open-access benchmark so that other researchers can utilize it to test a wider range of scoring functions. The complete CASF-2016 benchmark will be released on the PDBbind-CN web server ( http://www.pdbbind-cn.org/casf.asp/ ) once this article is published.

摘要

在基于结构的药物设计中,通常使用评分函数来评估蛋白质-配体相互作用。到目前为止,已经开发了多种评分函数,因此需要一些客观的基准来评估它们的优缺点。我们开发的比较性评估评分函数 (CASF) 基准满足了这一需求。CASF 被设计为一种“评分基准”,其中评分过程与对接过程解耦,以更精确地描绘评分函数的性能。在这里,我们描述了该基准的最新更新,即 CASF-2016。每个评分函数仍然通过四个指标进行评估,包括“评分能力”、“排序能力”、“对接能力”和“筛选能力”。然而,在几个方面,评估方法已经得到了相当大的改进。编译了一个新的测试集,其中包含 285 个具有高质量晶体结构和可靠结合常数的蛋白质-配体复合物。作为演示,对 CASF-2016 测试了 25 个评分函数。我们的结果表明,当前评分函数在对接能力方面的性能比评分、排序和筛选能力更有希望。评分能力与排序能力有些相关,对接能力和筛选能力也是如此。在 CASF-2016 上获得的结果可以为最终用户在可用评分函数之间做出明智选择提供有价值的指导。此外,CASF 被创建为一个开放访问的基准,以便其他研究人员可以利用它来测试更广泛的评分函数。本文发表后,完整的 CASF-2016 基准将在 PDBbind-CN 服务器 (http://www.pdbbind-cn.org/casf.asp/) 上发布。

相似文献

[1]
Comparative Assessment of Scoring Functions: The CASF-2016 Update.

J Chem Inf Model. 2018-12-11

[2]
Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.

Acc Chem Res. 2017-2-9

[3]
Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results.

J Chem Inf Model. 2014-6-23

[4]
Development of a new benchmark for assessing the scoring functions applicable to protein-protein interactions.

Future Med Chem. 2018-6-28

[5]
Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark.

Nat Protoc. 2018-3-8

[6]
Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set.

J Chem Inf Model. 2014-6-23

[7]
Comparative assessment of scoring functions on a diverse test set.

J Chem Inf Model. 2009-4

[8]
Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark.

J Chem Inf Model. 2018-7-25

[9]
Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information.

J Phys Chem B. 2023-10-26

[10]
Assessing multiple score functions in Rosetta for drug discovery.

PLoS One. 2020-10-12

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