National Laboratory for Scientific Computing - LNCC , Petrópolis , Rio de Janeiro 25651-075 , Brazil.
J Chem Inf Model. 2020 Feb 24;60(2):667-683. doi: 10.1021/acs.jcim.9b00905. Epub 2020 Jan 27.
Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .
蛋白质-肽相互作用在许多细胞和生物功能中起着至关重要的作用，这也 justifies 了人们对基于肽的药物开发的兴趣日益增加。然而，由于这类配体的一些特殊性，如高度的灵活性，大多数对接程序在预测蛋白质-肽对接中的实验结合模式和亲和力方面仍然面临巨大挑战。在本文中，我们展示了 DockThor 程序在 LEADS-PEP 数据集上的性能，该数据集是一个基准数据集，由 53 个不同的蛋白质-肽复合物组成，肽段长度从 3 到 12 个残基，旋转键多达 51 个。DockThor 在重对接研究中的构象预测性能与一些最先进的对接程序进行了比较，这些程序也在 LEADS-PEP 数据集上进行了评估，包括 AutoDock、AutoDock Vina、Surflex、GOLD、Glide、rDock 和 DINC，以及特定于任务的对接协议 HPepDock。我们的结果表明，当考虑得分最高的对接构象时，DockThor 可以对接 40%的情况，总体骨架 RMSD 低于 2.5 Å，表现出与 Glide 相似的结果，优于其他蛋白质-配体对接程序，而 rDock 和 HPepDock 则取得了更好的结果。评估与晶体结构最接近的对接构象（即最佳 RMSD 构象），DockThor 在构象预测中的成功率为 60%。由于其处理肽类化合物的整体性能出色，DockThor 程序可用于对接高度灵活和具有挑战性的配体，最多可达 40 个可旋转键。DockThor 可作为虚拟筛选 Web 服务器免费使用，网址为 https://www.dockthor.lncc.br/ 。
