Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China.
J Chem Theory Comput. 2023 Aug 22;19(16):5633-5647. doi: 10.1021/acs.jctc.3c00507. Epub 2023 Jul 22.
Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation ( = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.
核酸（NA）-配体相互作用在多种生物过程中至关重要，包括细胞复制和蛋白质生物合成，因此，NA 已被广泛认为是潜在的药物靶点。在原子尺度上理解 NA-配体相互作用对于研究分子机制和进一步协助针对 NA 的药物发现至关重要。分子对接是预测 NA 与小分子相互作用的主要计算方法之一。尽管有多种功能强大的对接程序，但它们在 NA-配体复合物方面的性能特征尚未得到充分表征。在这项研究中，我们首先编制了迄今为止最大的基于结构的 NA-配体结合数据集，其中包含 800 个具有明确鉴定配体的非共价 NA-配体复合物。基于这个广泛的数据集，我们系统地评估了八个常用的对接程序，包括六个蛋白-配体对接程序（LeDock、Surflex-Dock、UCSF Dock6、AutoDock、AutoDock Vina 和 PLANTS）和两个特定的 NA-配体对接程序（rDock 和 RLDOCK），分别从结合构象和结合亲和力预测两个方面进行评估。结果表明，一些蛋白-配体对接程序，特别是 PLANTS 和 LeDock，与专门的 NA-配体对接程序相比，产生了更有希望或相当的结果。在评估的程序中，PLANTS、rDock 和 LeDock 在结合构象预测方面表现最佳，其 top-1 和最佳均方根偏差（rmsd）成功率如下：PLANTS（35.93%和 76.05%）、rDock（27.25%和 72.16%）和 LeDock（27.40%和 64.37%）。与中等水平的结合构象预测相比，很少有程序在结合亲和力预测方面取得成功，与 PLANTS 观察到的最佳相关性（= -0.461）。最后，在四个已建立的数据集上与最新的 NA-配体对接程序（NLDock）进行进一步比较表明，PLANTS 和 LeDock 在所有数据集的结合构象预测方面均优于 NLDock，这表明它们在 NA-配体对接方面具有很大的潜力。据我们所知，这是对流行的分子对接程序进行的最全面的 NA-配体系统评估。
