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

Abstract

AutoDock Vina (Vina) achieved a very high docking‐success rate, truep^, 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 truep^ 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 RVina0.25em1.2=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 (Rnormalset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina0.25em1.2=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.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mproviaphysics- and knowledge-based approaches

“…In particular, the ligand-binding pose was delivered by molecular docking simulations instead of experimental data. 22 The equilibrated conformation of the complex could be effectively obtained using unbiased MD simulations. 91 Moreover, a previous benchmark indicated that SMD simulations provided more accurate results when the initial conformation was the MD-refined structure.…”

“…The metric can be determined via computation, and several methods have been established to solve the issues. 21 Numerous physics-based approaches have been designed, such as molecular docking simulations, 22,23 linear interaction energy (LIE), 24 fast pulling ligand (FPL), 25 molecular mechanics-Poisson–Boltzmann (generalized Born) surface area (MM-PB(GB)SA), 26,27 nonequilibrium molecular dynamics (NEMD), 28 thermodynamic integration, 29 free energy perturbation (FEP), 30 replica-exchange free energy perturbation (REP) 20 methods, etc. Moreover, knowledge-based methods also play a crucial role in estimating the binding affinity of a ligand to a protein, such as the quantitative structure–activity relationship (QSAR) 31,32 or machine learning (ML) methods.…”

MedChemExpress compounds prevent neuraminidase N1 via physics- and knowledge-based methods

“…Molecular docking simulations were usually performed to preliminary probe the binding affinities and poses of the ligands to receptors. 57,58 In this work, AutoDock Vina with the modified empirical parameters 43 was executed to explore the ligand-binding pose and affinity to N1. Initially, a benchmark was carried out to assess the performance of the docking protocol.…”

Adsorption of Diclofenac and PFBS on a Hair Keratin Dimer

“…AutoDock VINA 58 (version 1.1.2) was used to obtain and investigate binding site predictions. While docking programs in general are great tools for determining the geometric orientation or pose of a ligand in a binding site, the correlation coefficient of the docking score returned by AutoDock VINA and the experimental binding affinity is rather limited (≈0.493) 59,60 but comparable to other docking programs. 60 Due to the large extent of the K35/K85 keratin heterodimer and the presence of charged residues in its sequences, it is expected that more than a single site per heterodimer will be found.…”

“…AutoDock VINA (version 1.1.2) was used to obtain and investigate binding site predictions. While docking programs in general are great tools for determining the geometric orientation or pose of a ligand in a binding site, the correlation coefficient of the docking score returned by AutoDock VINA and the experimental binding affinity is rather limited (≈0.493) , but comparable to other docking programs …”