Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), China.
J Chem Theory Comput. 2020 Jun 9;16(6):3959-3969. doi: 10.1021/acs.jctc.9b01208. Epub 2020 May 6.
A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein-peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein-peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein-protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein-peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.
大量蛋白质-蛋白质相互作用（PPIs）是由蛋白质与肽段结合伴侣之间的相互作用介导的，因此确定蛋白质-肽相互作用（PpIs）对于阐明重要的生物学过程和设计能够调节 PPIs 的肽或肽模拟药物非常关键。如今，作为一种强大的计算工具，分子对接已被广泛用于预测蛋白质-肽复合物的结合结构。然而，尽管已经有许多对接程序可用，但从未有系统地研究评估它们在 PpIs 中的性能的报道。在这项研究中，使用了一个名为 PepSet 的基准数据集，其中包含 185 个具有 5 到 20 个残基的肽长度的蛋白质-肽复合物，用于评估 14 个对接程序的性能，包括三个蛋白质-蛋白质对接程序（ZDOCK、FRODOCK 和 HawkDock），三个小分子对接程序（GOLD、Surflex-Dock 和 AutoDock Vina）和八个蛋白质-肽对接程序（GalaxyPepDock、MDockPeP、HPEPDOCK、CABS-dock、pepATTRACT、DINC、AutoDock CrankPep（ADCP）和 HADDOCK 肽对接）。提出了一个新的评估参数，命名为 IL_RMSD，用于测量对接精度与（天然接触的分数）。在全局对接中，HPEPDOCK 在整个数据集上表现最好，在 top 1、10 和 100 水平的成功率分别为 4.3%、24.3%和 55.7%。在局部对接中，总体而言，ADCP 达到了最佳预测，在 top 1、10 和 100 水平的成功率分别为 11.9%、37.3%和 70.3%。我们希望我们的工作可以为选择和开发用于 PpIs 的改进对接程序提供一些有用的见解。基准数据集可在 http://cadd.zju.edu.cn/pepset/ 免费获取。
