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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis
COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 ant...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683650/ https://www.ncbi.nlm.nih.gov/pubmed/33230180 http://dx.doi.org/10.1038/s41598-020-77524-x |
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author | Nand, Mahesha Maiti, Priyanka Joshi, Tushar Chandra, Subhash Pande, Veena Kuniyal, Jagdish Chandra Ramakrishnan, Muthannan Andavar |
author_facet | Nand, Mahesha Maiti, Priyanka Joshi, Tushar Chandra, Subhash Pande, Veena Kuniyal, Jagdish Chandra Ramakrishnan, Muthannan Andavar |
author_sort | Nand, Mahesha |
collection | PubMed |
description | COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC(50) values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2. |
format | Online Article Text |
id | pubmed-7683650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76836502020-11-24 Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis Nand, Mahesha Maiti, Priyanka Joshi, Tushar Chandra, Subhash Pande, Veena Kuniyal, Jagdish Chandra Ramakrishnan, Muthannan Andavar Sci Rep Article COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC(50) values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2. Nature Publishing Group UK 2020-11-23 /pmc/articles/PMC7683650/ /pubmed/33230180 http://dx.doi.org/10.1038/s41598-020-77524-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Nand, Mahesha Maiti, Priyanka Joshi, Tushar Chandra, Subhash Pande, Veena Kuniyal, Jagdish Chandra Ramakrishnan, Muthannan Andavar Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title | Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title_full | Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title_fullStr | Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title_full_unstemmed | Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title_short | Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
title_sort | virtual screening of anti-hiv1 compounds against sars-cov-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683650/ https://www.ncbi.nlm.nih.gov/pubmed/33230180 http://dx.doi.org/10.1038/s41598-020-77524-x |
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