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Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.
- Source :
- Interdisciplinary Sciences: Computational Life Sciences; Sep2020, Vol. 12 Issue 3, p368-376, 9p
- Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- SARS-CoV-2
DEEP learning
RNA viruses
PEPTIDE drugs
PROTEOLYTIC enzymes
Subjects
Details
- Language :
- English
- ISSN :
- 19132751
- Volume :
- 12
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Interdisciplinary Sciences: Computational Life Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 145029331
- Full Text :
- https://doi.org/10.1007/s12539-020-00376-6