ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstraβe 43, 20146 Hamburg, Germany.
J Chem Inf Model. 2020 Dec 28;60(12):6502-6522. doi: 10.1021/acs.jcim.0c01095. Epub 2020 Dec 1.
Scoring and numerical optimization of protein-ligand poses is an integral part of docking tools. Although many scoring functions exist, many of them are not continuously differentiable and they are rarely explicitly analyzed with respect to their numerical optimization behavior. Here, we present a consistent scheme for pose scoring and gradient-based pose optimization. It consists of a novel variant of the BFGS algorithm enabling step-length control, named LSL-BFGS (limited step length BFGS), and the empirical JAMDA scoring function designed for pose prediction and good numerical optimizability. The JAMDA scoring function shows a high pose prediction performance in the CASF-2016 docking power benchmark, top-ranking a pose with an RMSD of ≤2 Å in about 89% of the cases. The combination of JAMDA scoring with the LSL-BFGS algorithm shows a significantly higher optimization locality (i.e., no excessive movement of poses) than with the classical BFGS algorithm while retaining the characteristically low number of scoring function evaluations. The JAMDA scoring and optimization scheme is freely available for noncommercial use and academic research.
蛋白质-配体构象的评分和数值优化是对接工具的一个组成部分。尽管存在许多评分函数，但其中许多函数不是连续可微的，而且它们的数值优化行为很少被明确分析。在这里，我们提出了一种一致的构象评分和基于梯度的构象优化方案。它由一种新的 BFGS 算法变体组成，能够控制步长，命名为 LSL-BFGS（有限步长 BFGS），以及为构象预测和良好的数值可优化性而设计的经验 JAMDA 评分函数。JAMDA 评分函数在 CASF-2016 对接能力基准测试中表现出很高的构象预测性能，在大约 89%的情况下，将 RMSD ≤2 Å 的构象排名第一。JAMDA 评分与 LSL-BFGS 算法的结合比经典 BFGS 算法具有更高的优化局部性（即构象的过度移动），同时保持特征性的低评分函数评估次数。JAMDA 评分和优化方案可供非商业用途和学术研究免费使用。
