DP Technology, Beijing, 100080, China.
Brief Bioinform. 2023 Jul 20;24(4). doi: 10.1093/bib/bbad218.
Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in drug discovery. Molecular mechanics/Generalized-Born (Poisson-Boltzmann) surface area (MM/GB(PB)SA) is one of the most popular methods for binding free energy calculations. It is more accurate than most scoring functions and more computationally efficient than alchemical free energy methods. Several open-source tools for performing MM/GB(PB)SA calculations have been developed, but they have limitations and high entry barriers to users. Here, we introduce Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations, which can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment. Uni-GBSA is available as an open-source package at https://github.com/dptech-corp/Uni-GBSA. It can also be accessed for virtual screening from the Hermite web platform at https://hermite.dp.tech. A free Uni-GBSA web server of a lab version is available at https://labs.dp.tech/projects/uni-gbsa/. This increases user-friendliness because the web server frees users from package installations and provides users with validated workflows for input data and parameter settings, cloud computing resources for efficient job completions, a user-friendly interface and professional support and maintenance.
配体与蛋白质受体的结合自由能计算是药物发现的基本目标。分子力学/广义 Born(泊松-玻尔兹曼)表面积(MM/GB(PB)SA)是结合自由能计算中最流行的方法之一。它比大多数评分函数更准确,比基于变分的自由能方法更具计算效率。已经开发了几种用于执行 MM/GB(PB)SA 计算的开源工具,但它们对用户具有限制和高进入门槛。在这里,我们介绍了 Uni-GBSA,这是一种用户友好的自动工作流程,用于执行 MM/GB(PB)SA 计算,它可以执行拓扑准备、结构优化、结合自由能计算和 MM/GB(PB)SA 计算的参数扫描。它还提供批处理模式,可在并行中针对一个蛋白质靶标评估数千个分子,以便在虚拟筛选中高效应用。默认参数是在对 PDBBind-2011 精制数据集进行系统测试后选择的。在我们的案例研究中,Uni-GBSA 与实验结合亲和力产生了令人满意的相关性,并在分子富集方面优于 AutoDock Vina。Uni-GBSA 可作为开源软件包在 https://github.com/dptech-corp/Uni-GBSA 上获得。它也可以从 Hermite 网络平台(https://hermite.dp.tech)访问以进行虚拟筛选。还可以在 https://labs.dp.tech/projects/uni-gbsa/ 访问免费的 Uni-GBSA 实验室版本网络服务器。这增加了用户友好性,因为网络服务器免除了用户进行软件包安装的麻烦,并为用户提供了经过验证的输入数据和参数设置工作流程、用于高效作业完成的云计算资源、用户友好的界面以及专业的支持和维护。