Molecular docking studies were performed for the selected antiviral phytochemical compounds with the selected target proteins of SARS CoV-2 by an automated docking tool, AutoDock Vina 4.2. AutoDock Vina 4.2 employed empirical free energy and a Lamarckian Genetic Algorithm. Docking simulation was repeated. During virtual screening with AutoDock Vina 4.2 complex formation was achieved by allowing all rotatable bonds of the chemical ligands free choice of torsional degrees of freedom and Rapid Grid-Based (RGB) energy evaluation (Rasool et al., 2020) .
Vina 4
Manufactured by AutoDock
Sourced in United States
AutoDock Vina 4.2 is a molecular docking software tool. It is designed to predict how small molecules, such as drug candidates, bind to a target protein of known three-dimensional structure. The software employs a hybrid global-local search algorithm to efficiently explore the rotational and translational degrees of freedom of the ligand. AutoDock Vina 4.2 provides a fast and accurate prediction of binding affinity.
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Lab products found in correlation
Tools, by AutoDock (5 mentions)
Tools 1, by AutoDock (4 mentions)
Discovery studio visualizer v20, by Dassault Systèmes (2 mentions)
Maestro tool, by Schrödinger (2 mentions)
Discovery studio 2019, by Dassault Systèmes (1 mentions)
Discovery studio visualizer, by Dassault Systèmes (1 mentions)
Dnaman version 6, by Lynnon Biosoft (1 mentions)
Marvinsketch 18, by ChemAxon (1 mentions)
Spartan 10, by Wavefunction (1 mentions)
Discovery studio, by Dassault Systèmes (1 mentions)
Schrodinger maestro, by Schrödinger (1 mentions)
Pymol molecular graphics system version 2, by Schrödinger (1 mentions)
Discovery studio client 2019, by Dassault Systèmes (1 mentions)
Accelrys discovery studio visualizer, by Dassault Systèmes (1 mentions)
55 protocols using vina 4
1
Molecular Docking of SARS-CoV-2 Antivirals
2
Lung cancer chemotherapy via thiazolo[3,2-a]pyrimidine
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Lung cancer, Thiazolo[3,2-a]pyrimidine, Urethane, Anti-inflammatory, Anti-proliferative, Ethyl carbamate function. To dock the ligand with desire proteins (kcal/ mol), we used AutoDock Vina 4.1 [22] [23] [24] [25] . AutoDock Vina 4.1 [25] calculated the predicted binding energy and expressed it in kcal/mol [26] . The interaction between these complexes was analyzed using the DS visualizer, and the potential hydrogen bonds, hydrophobic amino acid interactions in close proximity were also examined.
3
Molecular Docking of DB2277 with G-hp1 DNA
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DB2277 was optimized at the B3LYP/6-31*G level of theory using Spartan 10 software (Wavefunction, Inc.) (42 ). All minimized ligands were assigned Gasteiger−Huckel charges by using Autodock vina 4.02 (43 (link)). G-hp1 duplex DNA was generated from the biopolymer-build DNA double helix from the Tripos SYBYL-X1.2 software package (44 (link)). G-hp1 was docked with minimized structure of DB2277 using Autodock vina 4.02 (44 (link)). Further description is provided in detail in Supplementary Information text.
4
Docking of K-Ras Inhibitors and Natural Compounds
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The three dimensional structures of the G12D mutated K-ras structure inhibitor bound to SOS pocket (PDB ID: 4DST), and two G12C mutated K-ras structure bound to allosteric sites (PDB ID: 4LUC and 4LYF). Autodock Vina 4.2 [27 (link)] was used to dock all ligands to the K-ras protein. Before that, we used Autodock tools downloaded from The Scripps Research Institute to prepare the ligand and protein file [29 , 30 (link)]. All water molecules were removed and Kollman charges added as described in the Autodock Vina 4.2 manual [31 , 32 (link)]. The grid box dimensions were obtained from the grid box widget by keeping the bound ligand sites as box centers. Control studies were performed with all ligand bound in the crystal structures before docking with test ligands from T. flagelliforme. Pheophorbide a and two related epimers were drawn in ChemSketch [33 ] based on reported structure by Lai et al. [22 (link)] while hexadecene and hexadecanoic acid structure were obtained from Pubchem. The 2D structures of the active constituents are tabulated in Table 1 .
5
Identifying Antiplatelet Drug Candidates
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The obtained potential antiplatelet drugs of IS patients were docked with six proteins in PAPGs. Molecular docking was performed using AutoDockTools 1.5.6 and AutoDock Vina 4.2. Briefly, the docking is as follows. Firstly, the core compound structure files (mol2 format) were downloaded from the PubChem database. ChemDraw was used to minimize the structure energy and convert the structure into a 3D structure. Then, the target crystal structure was obtained from the PDB database (https://www.pdb.org/x ) and imported into PyMOL 1.7.2.1 (https://pymol.org/2/x ) for dehydration and hydrogenation for ligand separation. Docking grid boxes were subsequently constructed in AutoDockTools 1.5.6 at the active site of each target protein and then saved in pdbqt format. Molecular docking of putative targets and active compounds using AutoDock Vina 4.2 and evaluating free binding energies. Finally, visualize and analyze the interaction and critical patterns between drugs and proteins using PyMOL and Discovery Studio 2020.
6
Molecular Docking of Melatonin and Pazopanib with LC3
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The binding mode and selectivity of melatonin and pazopanib for LC3 were analyzed using AutoDock Vina 4.0 (Trott and Olson 2010), which required the ligand and receptor in PDBqt format and the configuration file in txt format. The structure of LC3 was downloaded from National Center for Biotechnology Information (NCBI) PubChem (CID. 6918774). Pymol was used to verify the presence or absence of hydrogen, the stereochemistry of chiral carbons, number of rotatable bonds, and number of hydrogen acceptors and donors. The Gasteiger charge was designated, and the torsions for melatonin and pazopanib were permitted to rotate during the docking procedure by using AutoDock tools version 1.5.6. The ligands were saved in PDBqt format. The receptor was prepared in AutoDock tools version 1.5.6: polar hydrogens were assigned to the receptor, the grid-box was centered on LC3, and the dimensions were set to 30, 30, 30 Å (x, y, z) to contain all the active site residues within the box. The receptor was saved in PDBqt format.
7
Molecular Docking of Peptide-Elastase Interactions
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Molecular docking studies were performed at Discovery Studio 2019 (Accelrys, San Diego, CA, USA), with slight modifications that were described earlier (Liu, Su, Zhou, Zhang, Zheng, & Zhao, 2018 (link)). AutoDock Vina 4.0 was used to detect the molecular interaction between polypeptide and elastase. The 3D structure of peptides within 10 amino acids was generated by ChemDraw. The X-ray crystal structure of porcine elastase (PDB ID: 1ELB) was downloaded from RCSB Protein Data Bank (https://www.rcsb.org/) https://pyrx.sourceforge.io ) platform, elastase receptor proteins and ligands were prepared (Dallakyan & Olson, 2015 ). All amino acid residues remained rigid, the ligands were flexibly treated, and the docking box (40 Å × 40 Å × 40 Å) covered as many protein surfaces as possible during the docking process. A grid spacing of 0.375 Å was used to enclose the active site. Lamarckian genetic algorithm was used for docking calculation, and the search parameter was set to 100 times. Subsequently, the docking model with the lowest binding energy of ligand in the binding pocket of protein was selected as the best model. The Vina score was used as the predictive affinity of peptide binding to elastase (calculated in kcal/mol).
8
Triazole-linked Pyrazolo[1,5-a]Pyrimidine Glycohybrids
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The docking studies were carried out using various derived triazole-linked pyrazolo[1,5-a]pyrimidine-based glycohybrids with proposed binding pocket of X-ray crystallographic structure (Protein Data Bank ID: 3PP0 , resolution: 2.4 Å). Docking was performed using Autodock Vina 4.0, and the interaction between the ligands and protein after docking was visualized and analyzed using PyMol software. The Biovia Discovery Studio Visualizer v20.1.0.19295 was used for 2D visualization and detailed ligand interaction visualization. The Schrödinger Maestro tool was utilized for QSAR and SAR studies.
9
Molecular Docking of Elvitegravir in RSV CSC
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The molecular docking of EVG in the octameric RSV CSC model was performed using AutoDock VINA40 (link) (Supplementary Fig. 7 ). Docking was done using the inner subunit of proximal dimer bound to MK-2048 and viral DNA along with Mg++. The S150P substitution was generated in silico using Chimera41 (link) and it did not result in clashes with neighboring residues (Supplementary Fig. 8 ). The EVG structure was built in Chimera. The polar hydrogen atoms were added to the structure. The best pose sorted on the basis of VINA score and INSTI interactions in the active sites was selected. Docked structures were displayed in Discovery Studio Visualizer (BIOVIA).
10
Molecular Docking of Dietary Agents and Cancer Targets
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Molecular docking studies using Autodock Vina 4.0 with a PyRx interface have been conducted between dietary agents and cancer targets56 (link). The docking parameters were set as follows: The Lamarckian genetic algorithm was used as the docking program by setting the window size 10.057 (link), and the number of individuals in the population was set to 150. The maximum number of energy evaluations and the maximum number of generations was set to 25,000 and 27,000. For the survival of next-generation, the top individual was set to 1. The gene mutation rate was set to 0.02. The crossover rate was set to 0.8, and the cauchy beta was 1.0. Accordingly, the grid was set according to the binding pocket and the exhaustiveness was set to 8. After docking simulations, the binding energy cutoff was set between 3.0 and 11.0 kcal/mol. Bond angles, bond lengths, and hydrogen bonding interactions were analyzed using PyMOL58 and electrostatic bond distance was set to between 3.0–5.0 Å, the hydrophobic bond was set to range 3.0–5.0 Å, and the hydrogen bond was set to between 2.8 and 4.0 Å.
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