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Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov
A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high signifi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266118/ https://www.ncbi.nlm.nih.gov/pubmed/32488835 http://dx.doi.org/10.1007/s12539-020-00376-6 |
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author | Zhang, Haiping Saravanan, Konda Mani Yang, Yang Hossain, Md. Tofazzal Li, Junxin Ren, Xiaohu Pan, Yi Wei, Yanjie |
author_facet | Zhang, Haiping Saravanan, Konda Mani Yang, Yang Hossain, Md. Tofazzal Li, Junxin Ren, Xiaohu Pan, Yi Wei, Yanjie |
author_sort | Zhang, Haiping |
collection | PubMed |
description | A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein–ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12539-020-00376-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7266118 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-72661182020-06-02 Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov Zhang, Haiping Saravanan, Konda Mani Yang, Yang Hossain, Md. Tofazzal Li, Junxin Ren, Xiaohu Pan, Yi Wei, Yanjie Interdiscip Sci Short Communication A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein–ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12539-020-00376-6) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-06-01 2020 /pmc/articles/PMC7266118/ /pubmed/32488835 http://dx.doi.org/10.1007/s12539-020-00376-6 Text en © International Association of Scientists in the Interdisciplinary Areas 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Short Communication Zhang, Haiping Saravanan, Konda Mani Yang, Yang Hossain, Md. Tofazzal Li, Junxin Ren, Xiaohu Pan, Yi Wei, Yanjie Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title | Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title_full | Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title_fullStr | Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title_full_unstemmed | Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title_short | Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov |
title_sort | deep learning based drug screening for novel coronavirus 2019-ncov |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266118/ https://www.ncbi.nlm.nih.gov/pubmed/32488835 http://dx.doi.org/10.1007/s12539-020-00376-6 |
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