Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina

Abstract

Structure-based virtual high-throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the 'docking power'. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein-ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and > 100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1,8,25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values > 25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.

Approaching the Dimerization Mechanism of Small Molecule Inhibitors Targeting PD-L1 with Molecular Simulation

“…Autodock Vina was used for docking small molecule inhibitors into the PD-L1 dimer [ 42 ]. We built a 22 × 18 × 18 grid box with 1 Å grid spacing centering on inhibitors, and the energy range and exhaustiveness were set as 4 kcal/mol and 24, respectively [ 43 , 44 ]. According to the affinity evaluation by scoring function and binding mode analysis, the best mode was selected as the initial structure for molecular dynamics simulation [ 45 , 46 ].…”

Structural analysis of human G‐protein‐coupled receptor 17 ligand binding sites

“…We used ARG87 as a reference amino acid to generate a grid for site 1, whereas ARG129 as a reference amino acid to generate a grid for site 2. Glide standard precision mode and AutodockVina are used for docking, and top ligand poses were retained for structural refinements 46,47 . Both sites' three ligands were docked, and the scores were tabulated 44,48 …”

“…Glide standard precision mode and AutodockVina are used for docking, and top ligand poses were retained for structural refinements. 46,47 Both sites' three ligands were docked, and the scores were tabulated. 44,48…”

A Study Based on Network Pharmacology Decoding the Multi‐Target Mechanism of Duhuo Jisheng Decoction for the Treatment of Intervertebral Disc Degeneration

“…The parameters of the docking box are recorded in Table S3 . We then performed molecular docking using AutoDock Vina with docking set to be based on AutoDock4 force field and exhaustiveness set to 32 (when the exhaustiveness is greater than 25, more resource consumption can only bring little benefit to the scoring function; we set exhaustiveness to 32 to ensure accuracy when performing molecular docking) [ 32 ]. After molecular docking, the PLIP web tool ( https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index ) was used to analyze the protein-ligand interaction [ 33 ].…”