scholarly journals Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians

10.2196/14232 ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. e14232
Author(s):  
Sania Safdar Butt ◽  
Yasmin Badshah ◽  
Maria Shabbir ◽  
Mehak Rafiq
Keyword(s):  
Molecular Docking ◽  
San Francisco ◽  
Target Protein ◽  
Autodock Vina ◽  
Protein Database ◽  
Computational Docking ◽  
Chemical Systems ◽  
Modern Drug ◽  
User Friendly ◽  
Ucsf Chimera

In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.

scholarly journals Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians (Preprint)

2019 ◽  
Author(s):  
Sania Safdar Butt ◽  
Yasmin Badshah ◽  
Maria Shabbir ◽  
Mehak Rafiq
Keyword(s):  
Molecular Docking ◽  
San Francisco ◽  
Target Protein ◽  
Autodock Vina ◽  
Protein Database ◽  
Computational Docking ◽  
Chemical Systems ◽  
Modern Drug ◽  
User Friendly ◽  
Ucsf Chimera

UNSTRUCTURED In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.

scholarly journals AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Mario S. Valdés-Tresanco ◽  
Mario E. Valdés-Tresanco ◽  
Pedro A. Valiente ◽  
Ernesto Moreno
Keyword(s):  
Molecular Docking ◽  
Search Space ◽  
Docking Studies ◽  
Molecular Structures ◽  
Autodock Vina ◽  
Ligand Selectivity ◽  
Input Structure ◽  
Graphical Tool ◽  
Selectivity Studies ◽  
User Friendly

Abstract AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to accurately prepare the input structure files and to optimally define the search space, offering several alternatives and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it automatically with several predefined visualization schemes to aid in setting up the box defining the search space and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock’s functional versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS. Reviewers This article was reviewed by Alexander Krah and Thomas Gaillard.

Molecular Docking Studies of Enzyme Binding Drugs on Family of Cytochrome P450

2020 ◽  
Vol 12 (1) ◽  
pp. 83-87
Author(s):  
Rolly Yadav ◽  
Anwesh Pandey ◽  
Nidhi Awasthi ◽  
Anamika Shukla
Keyword(s):  
Molecular Docking ◽  
Binding Pocket ◽  
Docking Studies ◽  
Drug Binding ◽  
Binding Modes ◽  
Computational Docking ◽  
Molecular Events ◽  
Modern Drug ◽  
Autodock 4.0 ◽  
Almost All

The combination of experimental and computational strategies has been of great value in the identification and development of metabolism of drugs. Nowadays modern drug design, molecular docking methods are helpful in exploring the ligand conformations adopted within the binding sites of macro-molecular targets such as DNA, proteins, and enzymes, there by reducing cost, time and wayward efforts of chemist. Since the development of the algorithms in the 1980s, molecular docking became an important tool in drug discovery like investigation of crucial molecular events, including ligand binding modes and the corresponding intermolecular interactions that stabilize the ligand-receptor complex, can be conveniently performed. In present study we have tried to investigate the drug binding pocket of various cytochrome (CYP) enzymes found in humans. All structures of drugs are optimized at B3LYP/6-31** level of theory using Gaussian program suite. Docking of substrate-enzyme duo was done using AUTODOCK 4.0. Computational docking revealed that almost all drugs have same binding pocket with varied binding affinities due to change in interactions and interacting distance from heme prosthetic moiety with transition metal iron as chelating ion.

scholarly journals DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

2020 ◽  
Vol 21 (24) ◽  
pp. 9548
Author(s):  
Gabriele Macari ◽  
Daniele Toti ◽  
Andrea Pasquadibisceglie ◽  
Fabio Polticelli
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
State Of The Art ◽  
Scoring Function ◽  
Conformational Space ◽  
Solvent Accessible Surface Area ◽  
Autodock Vina ◽  
Wide Range ◽  
User Friendly ◽  
New Scoring

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Bioactive Antidiabetic Flavonoids from the Stem Bark of Cordia dichotoma Forst.: Identification, Docking and ADMET Studies

Molbank ◽  
10.3390/m1234 ◽  
2021 ◽  
Vol 2021 (2) ◽  
pp. M1234
Author(s):  
Nazim Hussain ◽  
Bibhuti Bhushan Kakoti ◽  
Mithun Rudrapal ◽  
Khomendra Kumar Sarwa ◽  
Ismail Celik ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
Mass Spectroscopy ◽  
Column Chromatography ◽  
Stem Bark ◽  
Antidiabetic Activity ◽  
Bark Extract ◽  
Computational Studies ◽  
Autodock Vina ◽  
Online Tool

Cordia dichotoma Forst. (F. Boraginaceae) has been traditionally used for the management of a variety of human ailments. In our earlier work, the antidiabetic activity of methanolic bark extract of C. dichotoma (MECD) has been reported. In this paper, two flavonoid molecules were isolated (by column chromatography) and identified (by IR, NMR and mass spectroscopy/spectrometry) from the MECD with an aim to investigate their antidiabetic effectiveness. Molecular docking and ADMET studies were carried out using AutoDock Vina software and Swiss ADME online tool, respectively. The isolated flavonoids were identified as 3,5,7,3′,4′-tetrahydroxy-4-methoxyflavone-3-O-L-rhamnopyranoside and 5,7,3′-trihydroxy-4-methoxyflavone-7-O-L-rhamnopyranoside (quercitrin). Docking and ADMET studies revealed the promising binding affinity of flavonoid molecules for human lysosomal α-glucosidase and human pancreatic α-amylase with acceptable ADMET properties. Based on computational studies, our study reports the antidiabetic potential of the isolated flavonoids with predictive pharmacokinetics profile.

Selection of oxypeucedanin as a potential antagonist from molecular docking analysis of HSP90

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua Oluwasegun Bamidele ◽  
George Oche Ambrose ◽  
Oluwaseun Suleiman Alakanse
Keyword(s):  
Molecular Docking ◽  
Liver Toxicity ◽  
Docking Study ◽  
Docking Studies ◽  
Molecular Docking Study ◽  
Docking Analysis ◽  
Computational Docking ◽  
Drug Candidates ◽  
Expression Rate ◽  
Client Proteins

AbstractHSP90 is observed as one of the copious molecular chaperones that play a key role in mediating appropriate folding, maturation, and firmness of many client proteins in cells. The expression rate of HSP90 in cancer cells is at a level of 2- to 10-fold higher than the 1- to 2-fold of its unstressed and healthy ones. To combat this, several inhibitors to HSP90 protein have been studied (such as geldanamycin and its derivative 17-AAG and 17-DMAG) and have shown some primary side effects including plague, nausea, vomiting, and liver toxicity, hence the search for the best-in-class inhibitor for this protein through in silico. This study is aimed at analyzing the inhibitory potency of oxypeucedanin-a furocoumarin derivations, which have been reported to have antipoliferative activity in human prostrate carcinoma DN145 cells, and three other drug candidates retrieved from the literature via computational docking studies. The results showed oxypeucedanin as the compound with the highest binding energy of −9.2 kcal/mol. The molecular docking study was carried out using PyRx, Auto Dock Vina option, and the target was validated to confirm the proper target and the docking procedure employed for this study.

scholarly journals Prediction of Anti-COVID 19 Therapeutic Power of Medicinal Moroccan Plants Using Molecular Docking

2021 ◽  
Vol 15 ◽  
pp. 117793222110091
Author(s):  
Badreddine Nouadi ◽  
Abdelkarim Ezaouine ◽  
Mariame El Messal ◽  
Mohamed Blaghen ◽  
Faiza Bennis ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Medicinal Plants ◽  
Drug Target ◽  
Cost Effective ◽  
Current Medical Research ◽  
New Drugs ◽  
Binding Affinities ◽  
Global Public Health ◽  
Public Health Challenge ◽  
Ucsf Chimera

The emerging pathogen SARS-CoV2 causing coronavirus disease 2019 (COVID-19) is a global public health challenge. To the present day, COVID-19 had affected more than 40 million people worldwide. The exploration and the development of new bioactive compounds with cost-effective and specific anti-COVID 19 therapeutic power is the prime focus of the current medical research. Thus, the exploitation of the molecular docking technique has become essential in the discovery and development of new drugs, to better understand drug-target interactions in their original environment. This work consists of studying the binding affinity and the type of interactions, through molecular docking, between 54 compounds from Moroccan medicinal plants, dextran sulfate and heparin (compounds not derived from medicinal plants), and 3CLpro-SARS-CoV-2, ACE2, and the post fusion core of 2019-nCoV S2 subunit. The PDB files of the target proteins and prepared herbal compounds (ligands) were subjected for docking to AutoDock Vina using UCSF Chimera, which provides a list of potential complexes based on the criteria of form complementarity of the natural compound with their binding affinities. The results of molecular docking revealed that Taxol, Rutin, Genkwanine, and Luteolin-glucoside have a high affinity with ACE2 and 3CLpro. Therefore, these natural compounds can have 2 effects at once, inhibiting 3CLpro and preventing recognition between the virus and ACE2. These compounds may have a potential therapeutic effect against SARS-CoV2, and therefore natural anti-COVID-19 compounds.

scholarly journals MOLECULAR DOCKING AND PHARMACOKINETIC PREDICTION OF HERBAL DERIVATIVES AS MALTASE-GLUCOAMYLASE INHIBITOR

2017 ◽  
Vol 10 (9) ◽  
pp. 392 ◽  
Author(s):  
Peter Juma Ochieng ◽  
Tony Sumaryada ◽  
Daniel Okun
Keyword(s):  
Molecular Docking ◽  
Surface Area ◽  
Docking Studies ◽  
Polar Surface Area ◽  
Autodock Vina ◽  
Standard Drug ◽  
Lipinski’S Rule ◽  
Structure Activity ◽  
Topological Polar Surface Area ◽  
High Binding Affinity

  Objective: To perform molecular docking and pharmacokinetic prediction of momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine herbal derivatives as maltase-glucoamylase (MGAM) inhibitors for the treatment of diabetes.Methods: The herbal derivatives and standard drug miglitol were docked differently onto MGAM receptor using AutoDock Vina software. In addition, Lipinski’s rule, drug-likeness, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were analyzed using Molinspiration, ADMET structure–activity relationship, and prediction of activity spectra for substances online tools.Results: Docking studies reveal that momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine derivatives have high binding affinity to the MGAM receptor (−7.8, −6.8, and −6.5 Kcal/Mol, respectively) as compared to standard drug miglitol (−5.3 Kcal/Mol). In addition, all the herbal derivatives indicate good bioavailability (topological polar surface area <140 Ȧ and Nrot <10) without toxicity or mutagenic effects.Conclusion: The molecular docking and pharmacokinetic information of herbal derivatives obtained in this study can be utilized to develop novel MGAM inhibitors having antidiabetic potential with better pharmacokinetic and pharmacodynamics profile.

scholarly journals Enzyme-Site Blocking Combined with Optimization of Molecular Docking for Efficient Discovery of Potential Tyrosinase Specific Inhibitors from Puerariae lobatae Radix

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2612 ◽  
Author(s):  
Haichun Liu ◽  
Yitian Zhu ◽  
Ting Wang ◽  
Jin Qi ◽  
Xuming Liu
Keyword(s):  
Molecular Docking ◽  
Structural Modification ◽  
Enzyme Inhibitors ◽  
Kojic Acid ◽  
Systematic Evaluation ◽  
Tyrosinase Inhibition ◽  
Computational Docking ◽  
Site Blocking ◽  
Leading Compounds ◽  
Positive Results

Enzyme inhibitors from natural products are becoming an attractive target for drug discovery and development; however, separating enzyme inhibitors from natural-product extracts is highly complex. In this study, we developed a strategy based on tyrosinase-site blocking ultrafiltration integrated with HPLC-QTOF-MS/MS and optimized molecular docking to screen tyrosinase inhibitors from Puerariae lobatae Radix extract. Under optimized ultrafiltration parameters, we previously used kojic acid, a known tyrosinase inhibitor, to block the tyrosinase active site in order to eliminate false-positive results. Using this strategy, puerarin, mirificin, daidzin and genistinc were successfully identified as potential ligands, and after systematic evaluation by several docking programs, the rank of the identified compounds predicted by computational docking was puerarin > mirificin > kojic acid > daidzin ≈ genistin, which agreed with the results of tyrosinase-inhibition assays. Structure-activity relationships indicated that C-glycosides showed better tyrosinase inhibition as compared with O-glycosides, with reduced inhibition achieved through the addition of glycosyl, which provides ideas about the screen of leading compounds and structural modification.

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