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.

scholarly journals Design of New Chlorochalcone Derivatives as Potential Breast Anticancer Compound Based on QSAR Analysis and Molecular Docking Study

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
Anita Dwi Puspitasari ◽  
Harno Dwi Pranowo ◽  
Endang Astuti ◽  
Tutik Dwi Wahyuningsih
Keyword(s):  
Molecular Docking ◽  
Correlation Coefficient ◽  
Biological Activities ◽  
Basis Set ◽  
Log P ◽  
Anticancer Activities ◽  
Mcf7 Cells ◽  
Molecular Docking Study ◽  
Chemical Structures ◽  
Cox 2

Quantitative structure-activity relationships (QSAR) proposes a model that relates the biological activities of drugs to their chemical structures, and the interaction between the drug and its target enzyme is revealed by molecular docking research. These studies were conducted on chalcone to produce a model that could design highly potent breast anticancer MCF7 cells. The compounds were optimized using ab initio using a basis set 6-31G, then their descriptors calculated using this method. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the six models generated was found to be the best with internal and external squared correlation coefficient (R2) of 0.743 and 0.744, respectively, adjusted squared correlation coefficient (adjusted R2) of 0.700, Standard estimate of error (SEE) of 0.198, Fcalc/Ftable of 6.423, and Predicted residual sum of squares (PRESS) of 1.177. The QSAR equation is pIC50 = 3.869 + (1.427 x qC1) + (4. 027 x qC10) + (0.856 x qC15) - (35.900 x ELUMO) + (0.208 x Log P). Hence, it can predict the breast anticancer activities of new chlorochalcones A-F. The compound with the best prediction was chlorochalcone A with pIC50 2.65 and IC50 value of 2.26 μM. The chlorochalcones A-F were able to bind to the main amino acid residues, namely Arg120 and Tyr355, on the active site of the COX-2 enzyme. These results could serve as a model for designing novel chlorochalcone as inhibitors of COX-2 with higher breast anticancer activities. Keywords: Chlorochalcone, COX-2, QSAR, MCF-7, Molecular docking

scholarly journals Density Functional Theory and Molecular Docking Investigations of the Chemical and Antibacterial Activities for 1-(4-Hydroxyphenyl)-3-phenylprop-2-en-1-one

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3631
Author(s):  
Ahmed M. Deghady ◽  
Rageh K. Hussein ◽  
Abdulrahman G. Alhamzani ◽  
Abeer Mera
Keyword(s):  
Density Functional Theory ◽  
Antibacterial Activity ◽  
Molecular Docking ◽  
Carbonyl Group ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Reactivity ◽  
Basis Set ◽  
Molecular Docking Study ◽  
Functional Theory

The present investigation informs a descriptive study of 1-(4-Hydroxyphenyl) -3-phenylprop-2-en-1-one compound, by using density functional theory at B3LYP method with 6-311G** basis set. The oxygen atoms and π-system revealed a high chemical reactivity for the title compound as electron donor spots and active sites for an electrophilic attack. Quantum chemical parameters such as hardness (η), softness (S), electronegativity (χ), and electrophilicity (ω) were yielded as descriptors for the molecule’s chemical behavior. The optimized molecular structure was obtained, and the experimental data were matched with geometrical analysis values describing the molecule’s stable structure. The computed FT-IR and Raman vibrational frequencies were in good agreement with those observed experimentally. In a molecular docking study, the inhibitory potential of the studied molecule was evaluated against the penicillin-binding proteins of Staphylococcus aureus bacteria. The carbonyl group in the molecule was shown to play a significant role in antibacterial activity, four bonds were formed by the carbonyl group with the key protein of the bacteria (three favorable hydrogen bonds plus one van der Waals bond) out of six interactions. The strong antibacterial activity was also indicated by the calculated high binding energy (−7.40 kcal/mol).

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

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 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 Anticancer Screening of the Phytochemicals Present in the Medicinal Plant Vitex Negundo Against Mutant Anaplastic Lymphoma Kinase (ALK) Protein: An In-Silico Approach

2019 ◽  
Vol 12 (2) ◽  
pp. 993-1000 ◽  
Author(s):  
D. Anusha ◽  
S. Sharanya ◽  
Ramya Ramya ◽  
Darling Chellathai David
Keyword(s):  
Molecular Docking ◽  
Medicinal Plant ◽  
Anaplastic Lymphoma Kinase ◽  
Natural Compounds ◽  
Binding Energies ◽  
Vitex Negundo ◽  
Molecular Docking Study ◽  
Selective Toxicity ◽  
Molegro Virtual Docker ◽  
Anaplastic Lymphoma

The lymphomas are a heterogeneous group of cancer of the lymphocytes and the lymphatic system and accounts for up to 3% of all malignancies.1 Most of the drugs currently used for the treatment of lymphoma produce various side effects, hence in this study, we focus on natural compounds, obtained from the medicinal plant Vitex negundo, which exhibits selective toxicity against cancer cells. The objective of this research was to formulate the binding energies and interaction of selected phytochemicals present in the medicinal plant Vitex negundo2 against anaplastic lymphoma kinase protein, which is overexpressed in an anaplastic large cell lymphoma.3, 4,5 The structure of mutant human anaplastic lymphoma kinase protein was retrieved from the Protein Data Bank (PDB ID:4ANL ) and the 3D chemical structure of the phytochemicals present in the medicinal plant Vitex negundo was obtained from the PubChem database. Molecular docking study was performed for these natural compounds to evaluate and analyze their anti-lymphoma-cancer activity. A total of 16 compounds present in Vitex negundo, based on a comprehensive literature survey was selected for this molecular screening. Molecular docking analysis was carried out by Molegro Virtual Docker software, to screen the 16 chosen compounds and rank them according to their binding affinity towards the site of interaction of the oncoprotein, anaplastic lymphoma kinase. Out of the 16 screened phytocompounds, only 4 compounds showed promising interactions against the oncoprotein ALK (4ANL). 6’-p-hydroxybenzoyl mussaenosidic acid exhibited a very good binding with a molecular docking score of -127.723 kcal/mol, ranking first among the compounds screened. This was followed by Betulinic acid, Viridiflorol and protocatechuic acid with molecular docking scores of -95.596 kcal/mol, -76.1648 kcal/mol and -63.0854 kcal/mol and - respectively. The docking scores from the above study shows that the phytocompounds present in Vitex negundo extract exhibits an effective inhibitory effect against anaplastic lymphoma kinase protein that is over expressed in lymphoma.

scholarly journals MOLECULAR DOCKING STUDY TO IDENTIFY POTENTIAL INHIBITOR OF COVID-19 MAIN PROTEASE ENZYME: AN IN-SILICO APPROACH

2020 ◽  
Author(s):  
Anurag Agrawal ◽  
Nem Kumar Jain ◽  
Neeraj Kumar ◽  
Giriraj T Kulkarni
Keyword(s):  
Molecular Docking ◽  
Active Site ◽  
Docking Study ◽  
Molecular Docking Study ◽  
Potential Threat ◽  
Discovery Studio ◽  
Chemical Structures ◽  
Potential Inhibitor ◽  
Protease Enzyme ◽  
Main Protease

This study belongs to identification of suitable COVID-19 inhibitors<br><div><br></div><div>Coronavirus became pandemic very soon and is a potential threat to human lives across the globe. No approved drug is currently available therefore an urgent need has been developed for any antiviral therapy for COVID-19. For the molecular docking study, ten herbal molecules have been included in the current study. The three-dimensional chemical structures of molecules were prepared through ChemSketch 2015 freeware. Molecular docking study was performed using AutoDock 4.2 simulator and Discovery studio 4.5 was employed to predict the active site of target enzyme. Result indicated that all-natural molecules found in the active site of enzyme after molecular docking. Oxyacanthine and Hypericin (-10.990 and -9.05 and kcal/mol respectively) have shown good binding efficacy among others but Oxyacanthine was the only natural product which made some of necessary interactions with residues in the enzyme require for target inhibition. Therefore Oxyacanthine may be considered to be potential inhibitor of main protease enzyme of virus but need to be explored for further drug development process. <br></div>

scholarly journals Molecular Docking and Drug-Likeness for the Identification of Inhibitory Action of Acetogenins from Annona muricata as Potential Anticancer against Hypoxia Inducible Factor 1 Alpha

2018 ◽  
Vol 11 (3) ◽  
pp. 1301-1307
Author(s):  
Supri I. Handayani ◽  
Rahmiati Rahmiati ◽  
Lisnawati Rahmadi ◽  
Rosmalena Rosmalena ◽  
Vivitri D. Prasasty
Keyword(s):  
Molecular Docking ◽  
Hypoxia Inducible Factor ◽  
Binding Energies ◽  
Binding Modes ◽  
Annona Muricata ◽  
Chemical Structures ◽  
Hypoxia Inducible Factor 1 ◽  
Human Cancers

Hypoxia inducible factor 1 alpha (HIF-1α) regulates cell growth and differentiation which is implicated in human cancers. HIF-1α activates its cascade carcinogenesis mechanism in cancer cells. It is well-understood that signaling is initiated by HIF-1α receptor. Overexpression of HIF-1α is associated with several different human cancers, including breast cancer, lung cancer and colon cancer. Thus, HIF-1α becomes potential target of therapeutic approach in developing HIF-1α inhibitors. The aim of this research is to investigate potential inhibitors which are known as Acetogenins (AGEs) isolated from Annona muricata against HIF-1α. In order to achieve this goal, chemical structures of all compounds were retrieved from PubChem database. Molecular docking was performed by AutoDock Vina program and the resulting binding modes were analyzed with AutoDock Tools program. Among all the compounds, murihexocin A showed the best binding modes compared to other two inhibitors based on the lowest binding energies (LBE = -7.9 kcal/mol) as high as gefitinib. This was indicating that murihexocin A has favorable interaction with the essential amino acid residues at catalytic site of HIF-1α. Drug-likeness calculation of AGEs were also performed. These in silico results could be beneficial as a compound model for further studies in-vitro and in-vivo.

Potential Bioactive glycosylated flavonoids as SARS-CoV-2 Main protease Inhibitors: A molecular Docking Study

2020 ◽  
Author(s):  
sabri ahmed cherrak ◽  
merzouk hafida ◽  
mokhtari soulimane nassima
Keyword(s):  
Molecular Docking ◽  
Data Bank ◽  
Docking Study ◽  
Docking Studies ◽  
Molecular Docking Study ◽  
Chemical Structures ◽  
In Vivo Experiments ◽  
Main Protease

A novel (COVID-19) responsible of acute respiratory infection closely related to SARS-CoV has recently emerged. So far there is no consensus for drug treatment to stop the spread of the virus. Discovery of a drug that would limit the virus expansion is one of the biggest challenges faced by the humanity in the last decades. In this perspective, testing existing drugs as inhibitors of the main COVID-19 protease is a good approach.Among natural phenolic compounds found in plants, fruit, and vegetables; flavonoids are the most abundant. Flavonoids, especially in their glycosylated forms, display a number of physiological activities, which makes them interesting to investigate as antiviral molecules.The flavonoids chemical structures were downloaded from PubChem and protease structure 6lu7 was from the Protein Data Bank site. Molecular docking study was performed using AutoDock Vina. Among the tested molecules Quercetin-3-O-rhamnoside showed the highest binding affinity (-9,7 kcal/mol). Docking studies showed that glycosylated flavonoids are good inhibitors for the covid-19 protease and could be further investigated by in vitro and in vivo experiments for further validation.

Screening of Potent Inhibitors Against 2019 Novel Coronavirus (Covid-19) from Alliumsativum and Allium cepa: An In Silico Approach

2020 ◽  
Vol 11 (1) ◽  
pp. 7981-7993
Keyword(s):  
Molecular Docking ◽  
Allium Cepa ◽  
In Silico ◽  
Treatment Options ◽  
Natural Compounds ◽  
Binding Energies ◽  
Drug Candidate ◽  
Molecular Docking Study ◽  
Standard Drug ◽  
Main Protease

The infection of the global COVID-19 pandemic and the absence of any possible treatment options warrants the use of all available resources to find effective drugs against this scourge. Various ongoing researches have been searching for the new drug candidate against COVID-19 infection. The research objective is based on the molecular docking study of inhibition of the main protease of COVID-19 by natural compounds found in Allium sativum and Allium cepa. Lipinski rule of five and Autodock 4.2 was used by using the Lamarckian Genetic Algorithm to perform Molecular docking to analyze the probability of docking. Further, ADME analysis was also performed by using SwissADME, which is freely available on the web. In the present study, we identified S-Allylcysteine sulfoxide (Alliin), S-Propyl cysteine, S-Allylcysteine, S-Ethylcysteine, S-Allylmercaptocysteine, S-Methylcysteine, S-propyl L-cysteine with binding energies (-5.24, -4.49, -4.99, -4.91, -4.79, -4.76, -5.0 kcal/mol) as potential inhibitor candidates for COVID-19. Out of 7 selected compounds, alliin showed the best binding efficacy with target protein 6LU7. In silico ADME analysis revealed that these compounds are expected to have a standard drug-like property as well. Our findings propose that natural compounds from garlic and onion can be used as potent inhibitors against the main protease of COVID-19, which could be helpful in combating the COVID-19 pandemic.

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