Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Day Surgery Center, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac201.
Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.
预测小分子在蛋白质结合口袋中的天然或近天然结合构象是基于结构的药物设计中极其重要的任务，特别是在命中到先导和先导优化阶段。在这项研究中，开发了一个免费的、开放访问的 Web 服务器 fastDRH，用于预测和分析蛋白质-配体复合物结构。在 fastDRH 服务器中，AutoDock Vina 和 AutoDock-GPU 对接引擎、结构截断的 MM/PB(GB)SA 自由能计算程序和基于残基的多个构象的能量分解分析被很好地集成到一个用户友好和多功能的在线平台中。受益于模块化架构，用户可以灵活地使用三个功能中的一个或多个，包括分子对接、对接构象重新评分和热点残基预测，以根据 3Dmol.js 和 Apache ECharts 支持的结果分析面板清楚地获得关键信息。在蛋白质-配体结合模式预测方面，集成的结构截断 MM/PB(GB)SA 重新评分程序在基准测试中的成功率>80%，明显优于 AutoDock Vina（~70%）。对于热点残基鉴定，我们基于多个构象的残基能量分解分析策略比仅使用单个构象的方法更可靠，并且我们的解决方案在几个药物发现项目中得到了实验验证。总之，fastDRH 服务器是一种用于预测配体结合模式和蛋白质配体结合热点残基的有用工具。fastDRH 服务器可免费访问，网址为 http://cadd.zju.edu.cn/fastdrh/。
