Programme

P.32 -- Poster, VCTP-48

Date: Tuesday, 1 August 2023

Time: 08:30 - 10:00

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters

T. Ngoc Han Pham (1), Trung Hai Nguyen (2,3), Nguyen Minh Tam (3,4), Thien Y. Vu (1), Nhat Truong Pham (5), Nguyen Truong Huy (1), Binh Khanh Mai (6), Nguyen Thanh Tung (7,8), Minh Quan Pham (8,9), Van V. Vu (10), Son Tung Ngo (2,3)

(1) Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam (2) Laboratory of Theoretical and Computational Biophysics, Ton Duc Thang University, Ho Chi Minh City, Vietnam (3) Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam (4) Computational Chemistry Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam (5) Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam (6) Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA (7) Institute of Materials Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam (8) Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam (9) Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam (10) NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

AutoDock Vina (Vina) achieved a very high docking-success rate, p, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1= 0.556±0.025 compared with RDefault = 0.493±0.028 obtained by the original Vina and RVina 1.2 = 0.503 ± 0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R ≥ 0.500 for 32/48 targets, compared with the default package, giving R ≥ 0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1 = 0.617±0.017) than the default package (RDefault =0.543±0.020) and Vina version 1.2 (RVina 1.2 = 0.540± 0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

Presenter: Phạm Thị Ngọc Hân