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

被引:41
作者
Agarwal, Rupesh [1 ,2 ]
Smith, Jeremy C. [1 ,2 ]
机构
[1] Oak Ridge Natl Lab, UT ORNL Ctr Mol Biophys, POB 2009, Oak Ridge, TN 37831 USA
[2] Univ Tennessee, Dept Biochem & Cellular & Mol Biol, 14311 Cumberland Ave, Knoxville, TN 37996 USA
关键词
Virtual screening; Molecular docking; exhaustiveness; Vina; DOCKING; IDENTIFICATION; INHIBITORS;
D O I
10.1002/minf.202200188
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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.
引用
收藏
页数:5
相关论文
共 30 条
[1]   Repurposing potential of posaconazole and grazoprevir as inhibitors of SARS-CoV-2 helicase [J].
Abidi, Syed Hani ;
Almansour, Nahlah Makki ;
Amerzhanov, Daulet ;
Allemailem, Khaled S. ;
Rafaqat, Wardah ;
Ibrahim, Mahmoud A. A. ;
la Fleur, Philip ;
Lukac, Martin ;
Ali, Syed .
SCIENTIFIC REPORTS, 2021, 11 (01)
[2]   Application advances of deep learning methods for de novo drug design and molecular dynamics simulation [J].
Bai, Qifeng ;
Liu, Shuo ;
Tian, Yanan ;
Xu, Tingyang ;
Banegas-Luna, Antonio Jesus ;
Perez-Sanchez, Horacio ;
Huang, Junzhou ;
Liu, Huanxiang ;
Yao, Xiaojun .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 2022, 12 (03)
[3]   MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm [J].
Bai, Qifeng ;
Tan, Shuoyan ;
Xu, Tingyang ;
Liu, Huanxiang ;
Huang, Junzhou ;
Yao, Xiaojun .
BRIEFINGS IN BIOINFORMATICS, 2021, 22 (03)
[4]   Bioactive Compounds from Culinary Herbs Inhibit a Molecular Target for Type 2 Diabetes Management, Dipeptidyl Peptidase IV [J].
Bower, Allyson M. ;
Hernandez, Luis M. Real ;
Berhow, Mark A. ;
de Mejia, Elvira Gonzalez .
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2014, 62 (26) :6147-6158
[5]  
Computational Systems Biology Group, EBOXSIZE
[6]   Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins [J].
Devaurs, Didier ;
Antunes, Dinler A. ;
Hall-Swan, Sarah ;
Mitchell, Nicole ;
Moll, Mark ;
Lizee, Gregory ;
Kavraki, Lydia E. .
BMC MOLECULAR AND CELL BIOLOGY, 2019, 20 (01)
[7]  
eSilva E.F., 2020, ANTISARS COV EFFECT
[8]   Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets [J].
Feinstein, Wei P. ;
Brylinski, Michal .
JOURNAL OF CHEMINFORMATICS, 2015, 7
[9]   Computational protein-ligand docking and virtual drug screening with the AutoDock suite [J].
Forli, Stefano ;
Huey, Ruth ;
Pique, Michael E. ;
Sanner, Michel F. ;
Goodsell, David S. ;
Olson, Arthur J. .
NATURE PROTOCOLS, 2016, 11 (05) :905-919
[10]   Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark [J].
Gaillard, Thomas .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2018, 58 (08) :1697-1706