How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation

Dejun Jiang; Huifeng Zhao; Hongyan Du; Yafeng Deng; Zhenxing Wu; Jike Wang; Yundian Zeng; Haotian Zhang; Xiaorui Wang; Jian Wu; Chang-Yu Hsieh; Tingjun Hou

doi:10.1021/acs.jctc.3c00507

How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation

J Chem Theory Comput. 2023 Aug 22;19(16):5633-5647. doi: 10.1021/acs.jctc.3c00507. Epub 2023 Jul 22.

Authors

Dejun Jiang^{1

2}, Huifeng Zhao^{1

2}, Hongyan Du¹, Yafeng Deng², Zhenxing Wu¹, Jike Wang¹, Yundian Zeng¹, Haotian Zhang¹, Xiaorui Wang³, Jian Wu^{1

4}, Chang-Yu Hsieh¹, Tingjun Hou¹

Affiliations

¹ Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
² Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China.
³ China State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China.
⁴ College of Computer Science and Technology, Zhejiang University, Hangzhou 310006, Zhejiang, China.

PMID: 37480347
DOI: 10.1021/acs.jctc.3c00507

Abstract

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 (R_p = -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.

MeSH terms

Drug Discovery*
Ligands
Molecular Docking Simulation
Nucleic Acids*

Substances

Ligands
Nucleic Acids