Combination of Biodata Mining and Computational Modelling in Identification and Characterization of ORF1ab Polyprotein of SARS-CoV-2 Isolated from Oronasopharynx of an Iranian Patient

Combination of Biodata Mining and Computational Modelling in Identification and Characterization of ORF1ab Polyprotein of SARS-CoV-2 Isolated from Oronasopharynx of an Iranian Patient

Ramezan Ali Taheri, Reza Zolfaghari Emameh, Hassan Nosrati

Abstract Background Coronavirus disease 2019 (COVID-19) is an emerging zoonotic viral infection, which was started in Wuhan, China, in December 2019 and transmitted to other countries worldwide as a pandemic outbreak. Iran is one of the top ranked countries in the tables of COVID-19-infected and -mortality cases that make the Iranian patients as the potential targets for diversity of studies including epidemiology, biomedical, biodata, and viral proteins computational modelling studies. Results In this study, we applied bioinformatic biodata mining methods to detect CDS and protein sequences of ORF1ab polyprotein of SARS-CoV-2 isolated from oronasopharynx of an Iranian patient. Then through the computational modelling and antigenicity prediction approaches, the identified polyprotein sequence was analyzed. The results revealed that the identified ORF1ab polyprotein belongs to a part of nonstructural protein 1 (nsp1) with the high antigenicity residues in a glycine-proline or hydrophobic amino acid rich domain. Conclusions The results revealed that nsp1 as a virulence factor and crucial agent in spreading of the COVID-19 among the society can be a potential target for the future epidemiology, drug, and vaccine studies.

2020

nsp1, protein modelling, SARS-CoV-2, COVID-19, ORF1ab, Biodata mining

DOI: 10.1186/s12575-020-00121-9

Biological Procedures Online

BMC

Biology (General), Medicine (General)