Protein–ligand interaction study of signal transducer smoothened protein with different drugs: molecular docking and QM/MM calculations

RSC Advances ◽  
2015 ◽  
Vol 5 (84) ◽  
pp. 68829-68838 ◽  
Author(s):  
Hossein Farrokhpour ◽  
Vahid Pakatchian ◽  
Abdolreza Hajipour ◽  
Fatemeh Abyar ◽  
Alireza Najafi Chermahini ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Antitumor Agent ◽  
Signal Transducer ◽  
Interaction Study ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

A part of signal transducer smoothened (SMO) protein including antitumor agent LY2940680. The site of this antitumor was considered for the docking of 716 ligands.

scholarly journals Molecular docking studies of deacetylbisacodyl with intestinal sucrase-maltase enzyme

2017 ◽  
Vol 2 (12) ◽  
pp. 191 ◽  
Author(s):  
Ramchander Merugu ◽  
Uttam Kumar Neerudu ◽  
Karunakar Dasa ◽  
Kalpana V. Singh
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Electrostatic Interactions ◽  
Docking Studies ◽  
Amino Acid Residues ◽  
Interaction Study ◽  
Ligand Interaction ◽  
Docking Analysis ◽  
Protein Ligand Interaction ◽  
Intestinal Sucrase

Molecular docking of sucrase-isomaltase with ligand deacetylbisacodyl when subjected to docking analysis using docking server, predicted in-silico result with a free energy of -3.36 Kcal/mol which was agreed well with physiological range for protein-ligand interaction, making bisacodyl probable potent anti-isomaltase molecule. According to docking server Inhibition constant is 5.98Mm. which predicts that the ligand is going to inhibits enzyme and result in a clinically relevant drug interaction with a substrate for the enzyme. Hydrogen bond with bond length 3.45is formed between Pro 64 (A) of target and of ligand, which is again indicative of the docking between target and ligand. Excellent electrostatic interactions of polar, hydrophobic, pi-pi and Van der walls are observed. The proteinligand interaction study showed 6 amino acid residues interaction with the ligand.

Spiro-acridine inhibiting tyrosinase enzyme: Kinetic, protein-ligand interaction and molecular docking studies

2019 ◽  
Vol 122 ◽  
pp. 289-297 ◽  
Author(s):  
Thaís Meira Menezes ◽  
Sinara Mônica Vitalino de Almeida ◽  
Ricardo Olímpio de Moura ◽  
Gustavo Seabra ◽  
Maria do Carmo Alves de Lima ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Docking Studies ◽  
Enzyme Kinetic ◽  
Ligand Interaction ◽  
Molecular Docking Studies ◽  
Protein Ligand Interaction ◽  
Tyrosinase Enzyme

scholarly journals Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Docking ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Network Data ◽  
Ligand Interaction ◽  
Pubchem Compound ◽  
Protein Ligand Interaction

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>

scholarly journals Monolith weak affinity chromatography for μg-protein-ligand interaction study

2019 ◽  
Vol 166 ◽  
pp. 164-173 ◽  
Author(s):  
Lucile Lecas ◽  
Jérôme Randon ◽  
Alain Berthod ◽  
Vincent Dugas ◽  
Claire Demesmay
Keyword(s):  
Affinity Chromatography ◽  
Interaction Study ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

Protein-Ligand Interaction Study ofCpOGA in Complex with GlcNAcstatin

2012 ◽  
Vol 81 (2) ◽  
pp. 284-290 ◽  
Author(s):  
Paulo Robson M. Sousa ◽  
Nelson Alberto N. de Alencar ◽  
Anderson H. Lima ◽  
Jerônimo Lameira ◽  
Cláudio Nahum Alves
Keyword(s):  
Interaction Study ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals MOLECULAR DOCKING ANALYSIS OF NATRIURETIC PEPTIDE RECEPTOR-C TOWARDS THE DESIGN OF POTENTIAL ATRIAL FIBRILLATION INHIBITORS

2020 ◽  
Vol 9 (5) ◽  
pp. 2595-2600
Author(s):  
Shubhda Dev
Keyword(s):  
Atrial Fibrillation ◽  
Molecular Docking ◽  
Natriuretic Peptide ◽  
Antiarrhythmic Agents ◽  
Natriuretic Peptide Receptor ◽  
Ligand Interaction ◽  
Docking Analysis ◽  
Peptide Receptor ◽  
Protein Ligand Interaction ◽  
Molecular Docking Analysis

Atrial fibrillation (AF) stands the most widely recognized kind of clinical arrhythmia. Right now accessible anti-Atrial Fibrillation drugs are restricted by just moderate adequacy and an unfavorable safety profile. There is a perceived requirement for enhanced antiarrhythmic agents including activities that are specific for the fibrillating atrium. Therefore, it is of interest to design an appropriate medication for the disease Atrial Fibrillation using Molecular Docking techniques through protein-ligand interaction analysis. Hence, we document the Molecular docking analysis of natriuretic peptide receptor-C towards the design of potential Atrial Fibrillation inhibitors (Aprindine, Inclacumab, and Budiodarone) with the most favorable binding features for further consideration. This study centers around the process for drug discovery finding appropriate medication for the disease Atrial Fibrillation by Molecular Docking technique through protein-ligand interaction. The examination uncovered that out of a couple of molecules that were chosen as target, three of them were seen as most reasonable having the least energies compared to the other molecules. Aprindine, which is utilized in arrhythmia patients as a cardiac depressant. Inclacumab, which is an investigational sedate utilized in trials to look at the treatment and evasion of Myocardial Infarction, Peripheral Arterial Disease (PAD), and Coronary Heart Disease. Budiodarone, which is an antiarrhythmic drug at present in clinical preliminaries identified with amiodarone.

scholarly journals Compound2Drug – a Machine/deep Learning Tool for Predicting the Bioactivity of PubChem Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Docking ◽  
Drug Target ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Network Data ◽  
Ligand Interaction ◽  
Pubchem Compound ◽  
Protein Ligand Interaction

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>

Preliminary Identification of Hamamelitannin and Rosmarinic Acid as COVID-19 Inhibitors Based on Molecular Docking

2020 ◽  
Vol 17 ◽  
Author(s):  
Kaushik Sarkar ◽  
Rajesh Kumar Das
Keyword(s):  
Molecular Docking ◽  
Rosmarinic Acid ◽  
Virus Disease ◽  
Binding Energies ◽  
Basis Set ◽  
Global Public Health ◽  
Molecular Docking Study ◽  
Ligand Interaction ◽  
Chemical Structures ◽  
Protein Ligand Interaction

Background: Recently novel corona virus disease, COVID-19 caused the outbreak situation of global public health. In this pandemic situation, all the people lives of 212 Countries and Territories have been affected due to partial or complete lockdown and also as a result of mandatory isolations or quarantines. This is due to the non-availability of any secure vaccine. Objective: The present study helps us to identify and screen best phytochemicals as potent inhibitors against COVID-19. Methods: In this paper we choose two standard drugs namely hamamelitannin and rosmarinic acid as a probable inhibitor of pandemic COVID-19 receptor as compared to antimalarial drug hydroxychloroquine, anti-viral drug remdesivir and also baricitinib. This study was done by taking into consideration of molecular docking study, performed with Auto Dock 4.0 (AD4.0). All chemical structures were optimized with Avogadro suite by applying MMFF94 force field and also hamamelitannin, rosmarinic acid were optimized using Gaussian G16 suite of UB3LYP/6-311++G(d,p) basis set. Protein-ligand interaction was visualized by PyMOL software. Results and Discussion: This work has provided an insightful understanding of protein-ligand interaction of hamamelitannin and rosmarinic acid showing comparable binding energies than that of clinically applying probable COVID-19 inhibitors hydroxychloroquine (an anti-malarial drug) and remdesivir (an anti-viral drug). Conclusions: We will expect that if its anti-SARS-CoV-2 activity is validated in human clinical trials, these two drugs may be developed as an effective antiviral therapeutics towards infected patients in this outbreak and pandemic situation of COVID-19.

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