In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19
The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the conse...
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2021
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oai:doaj.org-article:fb365af8874843caa5e841101db42c8b2021-11-25T17:15:27ZIn Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-1910.3390/coatings111112732079-6412https://doaj.org/article/fb365af8874843caa5e841101db42c8b2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-6412/11/11/1273https://doaj.org/toc/2079-6412The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial.Reham F. BarghashIten M. FawzyVaisali ChandrasekarAjay Vikram SinghUma KathaAsmaa A. MandourMDPI AGarticleSARS CoV2vaccinesmolecular modelingdrug repurposingACE-2remdesevirEngineering (General). Civil engineering (General)TA1-2040ENCoatings, Vol 11, Iss 1273, p 1273 (2021) |
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SARS CoV2 vaccines molecular modeling drug repurposing ACE-2 remdesevir Engineering (General). Civil engineering (General) TA1-2040 |
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SARS CoV2 vaccines molecular modeling drug repurposing ACE-2 remdesevir Engineering (General). Civil engineering (General) TA1-2040 Reham F. Barghash Iten M. Fawzy Vaisali Chandrasekar Ajay Vikram Singh Uma Katha Asmaa A. Mandour In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
description |
The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial. |
format |
article |
author |
Reham F. Barghash Iten M. Fawzy Vaisali Chandrasekar Ajay Vikram Singh Uma Katha Asmaa A. Mandour |
author_facet |
Reham F. Barghash Iten M. Fawzy Vaisali Chandrasekar Ajay Vikram Singh Uma Katha Asmaa A. Mandour |
author_sort |
Reham F. Barghash |
title |
In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
title_short |
In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
title_full |
In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
title_fullStr |
In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
title_full_unstemmed |
In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19 |
title_sort |
in silico modeling as a perspective in developing potential vaccine candidates and therapeutics for covid-19 |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/fb365af8874843caa5e841101db42c8b |
work_keys_str_mv |
AT rehamfbarghash insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 AT itenmfawzy insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 AT vaisalichandrasekar insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 AT ajayvikramsingh insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 AT umakatha insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 AT asmaaamandour insilicomodelingasaperspectiveindevelopingpotentialvaccinecandidatesandtherapeuticsforcovid19 |
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