1001 Ways to run AutoDock Vina for virtual screening Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D.
Journal of computer-aided molecular design,
03/2016, Letnik:
30, Številka:
3
Journal Article
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Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to ...evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.
mtbDHFR‐targeting inhibition has become a promising approach for tuberculosis treatment. In the current research, a multi‐step virtual screening effort toward ZINC and MCE databases was devoted to ...discover novel mtbDHFR inhibitors. Based on binding affinity of small molecules through molecular docking study in AutoDock Vina, the number of compounds was reduced to 952,688. Further, these compounds were employed by a step‐by‐step multiple docking programs of Schrödinger suite and filtered by pharmacokinetics and PAINS parameters. Finally, nine ZINC compounds and 400 MCE compounds were obtained. These compounds of binding ability were tested with mtbDHFR by FluoPol‐ABPP approach established in this work. Finally, AF‐353 compound was found to have strong binding effect to mtbDHFR. AF‐353 was further tested for mtb and hDHFR enzymatic activities, and it was proved to possess 50‐fold selectivity toward mtbDHFR over hDHFR. In silico MD simulation results supported this selectivity.
A highly selective mtbDHFR inhitor was identified by combinational approaches of multiple virtual screening and wet experiments.
•Dipeptidyl peptidase-IV (DPP-IV) and xanthine oxidase (XO) inhibition were studied.•Molecular docking allowed prediction of Trp-Val as a potent inhibitor of DPP-IV.•The amino acids Trp, Leu and Met ...were competitive inhibitors of DPP-IV.•Trp and Trp-Val inhibited both XO and DPP-IV activity.•All 5 Trp-containing dipeptides tested inhibited XO in a non competitive manner.
Xanthine oxidase (XO) and dipeptidyl peptidase IV (DPP-IV) inhibition by amino acids and dipeptides was studied. Trp and Trp-containing dipeptides (Arg-Trp, Trp-Val, Val-Trp, Lys-Trp and Ile-Trp) inhibited XO. Three amino acids (Met, Leu and Trp) and eight dipeptides (Phe-Leu, Trp-Val, His-Leu, Glu-Lys, Ala-Leu, Val-Ala, Ser-Leu and Gly-Leu) inhibited DPP-IV. Trp and Trp-Val were multifunctional inhibitors of XO and DPP-IV. Lineweaver and Burk analysis showed that Trp was a non-competitive inhibitor of XO and a competitive inhibitor of DPP-IV. Molecular docking with Autodock Vina was used to better understand the interaction of the peptides with the active site of the enzyme. Because of the non-competitive inhibition observed, docking of Trp-Val to the secondary binding sites of XO and DPP-IV is required. Trp-Val was predicted to be intestinally neutral (between 25% and 75% peptide remaining after 60min simulated intestinal digestion). These results are of significance for the reduction of reactive oxygen species (ROS) and the increase of the half-life of incretins by food-derived peptides.
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.
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•Ethyl pyridine substituted 3-cyanothiophene is identified as best docked compound.•Protein residues, Thr42 and Asp43 are involved in stabilizing the complex.•Molecular dynamic ...simulation revealed stable nature of the complex.•RDF and novel AFD analysis further supported the overall stability of the complex.•MM(PB/GB)SA energy calculation revealed dominancy of van der Waals energies.
MurF ligase catalyzes the final cytoplasmic step of bacterial peptidoglycan biosynthesis and, as such, is a validated target for therapeutic intervention. Herein, we performed molecular docking to identify putative inhibitors of Acinetobacter baumannii MurF (AbMurF). Based on comparative docking analysis, compound 114 (ethyl pyridine substituted 3-cyanothiophene) was predicted to potentially be the most active ligand. Computational pharmacokinetic characterization of drug-likeness of the compound showed it to fulfil all the parameters of Muegge and the MDDR rule. A molecular dynamic simulation of 114 indicated the complex to be stable on the basis of an average root mean square deviation (RMSD) value of 2.09Å for the ligand. The stability of the complex was further supported by root mean square fluctuation (RMSF), beta factor and radius of gyration values. Analyzing the complex using radial distribution function (RDF) and a novel analytical tool termed the axial frequency distribution (AFD) illustrated that after simulation the ligand is positioned in close vicinity of the protein active site where Thr42 and Asp43 participate in hydrogen bonding and stabilization of the complex. Binding free energy calculations based on the Poisson-Boltzmann or Generalized–Born Surface Area Continuum Solvation (MM(PB/GB)SA) method indicated the van der Waals contribution to the overall binding energy of the complex to be dominant along with electrostatic contributions involving the hot spot amino acids from the protein active site. The present results indicate that the screened compound 114 may act as a parent structure for designing potent derivatives against AbMurF in specific and MurF of other bacterial pathogens in general.
MAGI-Dock: a PyMOL companion to Autodock Vina Kaftalli, J; Bernini, A; Bonetti, G ...
European review for medical and pharmacological sciences,
12/2023, Letnik:
27, Številka:
6 Suppl
Journal Article
Recenzirano
Molecular docking simulation of small molecule drugs to macromolecules is valuable in structural biology and medicinal chemistry research. Its spread is supported by freely available software and ...databases. Like many resources in the free domain, docking software is command-line based, which comes to a limitation when defining the volume encompassing an active site, the so-called docking box. The box center and size, usually specified as cartesian coordinates, can be adjusted to correctly cover the active site only with a third-party molecular graphics program compatible with the docking input/output files, which reduces the choice to a few options. Moreover, the additional staff training may hamper the adoption of such software, e.g., in an enterprise environment. We exposed the functionality of Autodock and Autodock Vina into a graphical user interface extending upon that of PyMOL. Both the functionality of PyMOL and Autodock are merged, synergizing the capabilities of each program. To overcome such limitations, here we present MAGI-Dock. This graphical user interface combines the power of two of the most used free software for docking and graphics, Autodock Vina and PyMOL. MAGI-Dock is a free open-source software available under the GPL and can be downloaded from https://github.com/gjonwick/MAGI-Dock. The coupling of Autodock Vina with PyMOL through a graphical interface removes the molecular modeling limitations that come with Autodock. Therefore, MAGI-Dock could be conducive to lowering the learning curve for molecular docking simulation, with benefits for trainees in both academia and enterprise environments.
Peptide therapeutics is proven to be highly potential in the treatment of various diseases due to its specificity, biological safety, and cost‐effectiveness. Many of the FDA‐approved peptides are ...currently available for therapeutic applications. In the current postgenomic era, high‐throughput computational screening of drugs and peptides are highly exploited in peptide therapeutics for cost‐effective and robustness. However, there is a paucity of efficient pipelines that automate virtual screening process of peptides through integration of open‐source tools that are optimal to perform ensemble and flexible docking protocols. Hence, in this study, we developed a GUI‐based pipeline named PepVis for automated script generation for large‐scale peptide modeling and virtual screening. PepVis integrates Modpep and Gromacs for peptide structure modeling and optimization; AutoDock Vina, ZDOCK, and AutoDock CrankPep for virtual screening of peptides; ZRANK2 for rescoring of protein–peptide complexes, and FlexPepDock for flexible refinement of protein–peptide complexes. Benchmarking of ensemble docking through PepVis infers that ModPep + Vina to outperform ModPep + ZDock in terms of detecting near‐natives from LEADS‐PEP dataset. PepVis is built modular to incorporate many other docking algorithms in the future. This pipeline is distributed freely under the GNU GPL license and can be downloaded at https://github.com/inpacdb/PepVis.
PepVis toolkit is a GUI‐based pipeline which can be used for large‐scale peptide modeling and virtual screening. PepVis integrates ModPep and Gromacs for peptide structure modeling and refinement. For virtual screening and scoring, it integrates Vina, ZDOCK, AutoDock CrankPep, ZRANK2, and FlexPepDock. PepVis is highly tuned for seamless automation of both ensemble and flexible docking protocols.
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular ...first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.
As an important theoretical computation method in computer-aided drug design, molecular docking has significantly shifted the paradigm of drug discovery. As one of the open-source docking software, ...Autodock Vina (Vina) is widely popular, but the lack of relevant experience and inappropriate docking parameters make it unable to perform optimally in practical application scenarios, which leads to potential failure risks in the early stage of drug discovery. In order to simplify the docking steps and determine the most appropriate docking parameters, a universal solution for rigid receptor docking using Vina has been proposed in this paper, and a user-friendly software for the entire process of molecular docking using Vina is developed. The case studies show that our docking solution is able to be applied to different docking scenarios to facilitate a more accurate, faster, and more convenient new drug discovery process.
•An accurate and universal batch docking solution using Autodock Vina is proposed.•The optimal parameters for batch docking with Vina are determined.•A user-friendly software interface is developed for docking with Vina.
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