Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States.
J Chem Inf Model. 2024 Feb 12;64(3):1004-1016. doi: 10.1021/acs.jcim.3c01406. Epub 2024 Jan 11.
Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
分子对接是一种广泛应用的技术，可利用蛋白质结构来发现配体，但由于尚未得到充分解决的限制，该技术仍然难以使用。尽管在自动化方面取得了一些进展，但对接仍然需要专家指导，这阻碍了更广泛的研究人员采用该技术。为了使对接更加容易，我们开发了一种名为 DockOpt 的新工具，该工具可在大规模前瞻性筛选之前自动创建、评估和优化对接模型。在 DUDE-Z 基准数据集的所有 43 个目标中，DockOpt 的性能均优于我们之前的自动化流水线，并且 84%的目标生成的模型具有足够的富集度，可以在前瞻性筛选中使用，归一化 LogAUC 值至少为 15%。DockOpt 作为 Python 包 Pydock3 的一部分提供，该包包含在 UCSF DOCK 3.8 分发版中，可在 https://dock.compbio.ucsf.edu 免费提供给学术研究人员，并在 https://tldr.docking.org 注册后免费提供给所有人。
