Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

• Published in: Interdisciplinary sciences : computational life sciences Vol. 12; no. 3; pp. 368 - 376
• Main Authors: Zhang, Haiping, Saravanan, Konda Mani, Yang, Yang, Hossain, Md. Tofazzal, Li, Junxin, Ren, Xiaohu, Pan, Yi, Wei, Yanjie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: Adenosine; Amino Acid Sequence; Antiviral Agents - chemistry; Antiviral Agents - pharmacology; Betacoronavirus - drug effects; Betacoronavirus - genetics; Biomedical and Life Sciences; Catalytic Domain; Chemical compounds; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Coronavirus 3C Proteases; Coronavirus Infections - drug therapy; Coronavirus Infections - epidemiology; Coronavirus Infections - virology; Coronaviruses; COVID-19; Cysteine Endopeptidases - chemistry; Cysteine Endopeptidases - genetics; D-Sorbitol; Databases, Nucleic Acid; Databases, Pharmaceutical; Deep Learning; Disease transmission; Drug Design; Drug development; Drug Evaluation, Preclinical - methods; Drug Evaluation, Preclinical - statistics & numerical data; Drug screening; Drugs; Ganciclovir; Gene sequencing; Health Sciences; Homology; Humans; Isoleucine; Libraries; Life Sciences; Ligands; Literature reviews; Lysine; Machine learning; Mannitol; Mathematical and Computational Physics; Medicine; Models, Molecular; Molecular Dynamics Simulation; Nucleotide sequence; Oligopeptides - chemistry; Oligopeptides - pharmacology; Pandemics; Pneumonia, Viral - drug therapy; Pneumonia, Viral - epidemiology; Pneumonia, Viral - virology; Proline; Protease; Proteinase; Proteins; Public domain; Ribonucleic acid; RNA; RNA viruses; SARS-CoV-2; Sequence Alignment; Sequence analysis; Short Communication; Sorbitol; Statistics for Life Sciences; Structural Homology, Protein; Structural models; Theoretical; Theoretical and Computational Chemistry; Three dimensional models; User-Computer Interface; Viral diseases; Viral Nonstructural Proteins - antagonists & inhibitors; Viral Nonstructural Proteins - chemistry; Viral Nonstructural Proteins - genetics; Viruses; China; Deep learning; 3C-like protease; Coronavirus; Drug screening; Homology modeling
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-020-00376-6

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.