Combining classical molecular docking with self-consistent charge density-functional tight-binding computations for the efficient and quality prediction of ligand binding structure. Issue 3 (May 2022)

• Record Type: Journal Article
• Authors: Al-Ansi, Amar Y; Lu, Haorui; Lin, Zijing
• Is Part Of: Journal of chemical research. Volume 46:Issue 3(2022)
• Publication Date: 2022-05
• Subjects: automated docking -- binding structure -- clustering analysis -- structure-based drug design; Chemistry -- Research -- Periodicals; Chemistry -- Periodicals; 540.5
• DOI: 10.1177/17475198221101999
• Languages: English
• ISSNs: 1747-5198

Abstract

To improve the successful prediction rate of the existing molecular docking methods, a new docking approach is proposed that consists of three steps: generating an ensemble of docked poses with a conventional docking method, performing clustering analysis of the ensemble to select the representative poses, and optimizing the representative structures with a low-cost quantum mechanics method. Three quantum mechanics methods, self-consistent charge density-functional tight-binding, ONIOM(DFT:PM6), and ONIOM(SCC-DFTB:PM6), are tested on 18 ligand-receptor bio-complexes. The rate of successful binding pose predictions by the proposed self-consistent charge density-functional tight-binding docking method is the highest, at 67%. The self-consistent charge density-functional tight-binding docking method should be useful for the structure-based drug design.