A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease

Hall, Donald C.; Ji, Hai Feng

doi:10.1016/j.tmaid.2020.101646

Cited by 269 publications

(241 citation statements)

References 13 publications

(10 reference statements)

Supporting

Mentioning

222

Contrasting

Unclassified

Order By: Relevance

“…Similarly, the third compound, phlorizin, showed conventional H bonds with GLN189(A), MET49(A), CYS145(A), MET165(A), GLY143(A), and SER144(A);van der Waals interaction with THR25(A), THR26(A), LEU27(A), TYR54(A), ASP187(A), HIS164(A), ARG188(A), HIS163(A), LEU141(A), ASN142(A). We have also compared the binding affinity of our compounds with three different reference compounds remdesivir, X77, and N3 for Mpro with previously reported docking scores of −7.2 kcal/mol, −8.23 kcal/mol, and −7.9 kcal/mol, respectively, which is near to our obtained docking scores of −7.9 kcal/mol, −7.5 kcal/mol, and−8.6 kcal/mol [ 21 , 22 , 23 ]. Remdesivir showed five conventional H bonds with THR199(A), ASP289(A), LYS137(A), ARG 131(A), LEU287(A); van der Waals interaction with ALA285(A), GLY275(A), LEU271(A), ASN238(A), THR198(A), ASP197(A), TYR239(A).…”

Section: Resultssupporting

confidence: 78%

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study

et al. 2020

View full text Add to dashboard Cite

The current pandemic, caused by SARS-CoV-2 virus, is a severe challenge for human health and the world economy. There is an urgent need for development of drugs that can manage this pandemic, as it has already infected 19 million people and led to the death of around 711,277 people worldwide. At this time, in-silico studies are providing lots of preliminary data about potential drugs, which can be a great help in further in-vitro and in-vivo studies. Here, we have selected three polyphenolic compounds, mangiferin, glucogallin, and phlorizin. These compounds are isolated from different natural sources but share structural similarities and have been reported for their antiviral activity. The objective of this study is to analyze and predict the anti-protease activity of these compounds on SARS-CoV-2main protease (Mpro) and TMPRSS2 protein. Both the viral protein and the host protein play an important role in the viral life cycle, such as post-translational modification and viral spike protein priming. This study has been performed by molecular docking of the compounds using PyRx with AutoDock Vina on the two aforementioned targets chosen for this study, i.e., SARS-CoV-2 Mpro and TMPRSS2. The compounds showed good binding affinity and are further analyzed by (Molecular dynamic) MD and Molecular Mechanics Poisson-Boltzmann Surface Area MM-PBSA study. The MD-simulation study has predicted that these natural compounds will have a great impact on the stabilization of the binding cavity of the Mpro of SARS-CoV-2. The predicted pharmacokinetic parameters also show that these compounds are expected to have good solubility and absorption properties. Further predictions for these compounds also showed no involvement in drug-drug interaction and no toxicity.

show abstract

Section: Resultssupporting

confidence: 78%

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Remdesivir is an antiviral drug that was found to show activity against the virus and it did inhibit the SARS-CoV-2 virus to certain extent. Remdesivir is presently used against COVID-19 in certain countries 7 .…”

Section: Introductionmentioning

confidence: 99%

Bioinformatic study to discover natural molecules with activity against COVID-19

Singh

Flórez

2020

F1000Res

View full text Add to dashboard Cite

Background: In 2020, the world has struggled to deal with coronavirus disease 2019 (COVID-19), which started in 2019 in China and has spread throughout the globe, affecting at least 31,175,835 humans globally and claiming 962,634 lives reported till 22nd September, 2020 by the World Health Organization. The main causative agent for this disease is known as severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). So far, there is no cure or proven therapeutics available till date. Therefore, we undertook this study to ﬁnd the most probable drug candidate through a bioinformatics study. Methods: Thus, we virtually screened the Zinc natural database using HTVS tool through molecular docking studies to analyze molecules recommended for the treatment of COVID-19. Results: Ramipril benzyl ester, propafenone dimer and Lariciresinol are three important drugs found from the present study due to their medicinal application which could be helpful in treating the disease. Stylopine, quillaic acid, cinobufagin, vitisinol C, segetalin A, scopolamine, 3-oxo glycyrrhetinic acid, conchinone B, lactimidomycin and cardinalins 4 are the other lead molecules that could be used as therapeutics against COVID-19 disease. Conclusions: The studied molecules could act as an effective inhibitory drug against COVID-19.

show abstract

“…intensive research to analyze it and to curb its ill-effects [6][7][8]. Apart from virologic and experimental studies, mathematical and statistical explorations are also important for the decision-makers and policy makers to evaluate the current situation and to take the necessary actions for the future.…”

Section: During These Hard Times Of the Pandemic Researchers From Vamentioning

confidence: 99%

Mathematical Modelling of Coronavirus disease (COVID-19) Outbreak in India using Logistic Growth and SIR Models

Mackolil¹,

Mahanthesh²

2020

Preprint

View full text Add to dashboard Cite

The mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India is done by using the logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework. Karnataka, Kerala and Maharashtra, three states of India, are selected based on the pattern of the disease spread and the prominence in being affected in India. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. The models classify the pandemic into five stages based on the nature of the infection growth rate. According to the estimates of the models it can be concluded that Kerala is in a stable situation whereas the pandemic is still growing in Karnataka and Maharashtra. The infection rate of Karnataka and Kerala are lesser than 5% and reveal a downward trend. On the other hand, the infection rate and the high predicted number of infectives in Maharashtra calls for more preventive measures to be imposed in Maharashtra to control the disease spread.

show abstract

A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease

Cited by 269 publications

References 13 publications

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study

Bioinformatic study to discover natural molecules with activity against COVID-19

Mathematical Modelling of Coronavirus disease (COVID-19) Outbreak in India using Logistic Growth and SIR Models

Contact Info

Product

Resources

About