Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Abstract

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein − ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.

Communicated by Ramaswamy H. Sarma

Keywords:

• Molecular docking
• semiempirical quantum mechanics
• docking pose optimization;
• PDBbind core set
• sampling and ranking powers

Acknowledgments

The authors are grateful to Mohsen Namazi (University of Tehran) for valuable comments on the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and software availability

REDUCE software, MOPAC2016, AutoDock4 (version 4.2.5.1), and AutoDock Vina (version 1.1.2) were used under a free academic license for protein structures preparation, ligands optimization, and docking simulations, respectively. All calculated data are available upon request from the corresponding authors.

Additional information

Funding

The authors are grateful to Mohsen Namazi (University of Tehran) for valuable comments on the project.