Binding site identification of COVID-19 main protease 3D structure by homology modeling

Adebiyi, Marion and Olugbara, Oludayo (2021) Binding site identification of COVID-19 main protease 3D structure by homology modeling. Indonesian Journal of Electrical Engineering and Computer Science, 21 (3). p. 1713. ISSN 2502-4752

Text COVID 19-Protease-published 2021 (2021_06_27 14_50_26 UTC).pdf - Published Version Download (442kB)

Abstract

The influx of coronavirus in 2019 (COVID-19) has recorded millions of infection cases with several deaths worldwide. There is no effective treatment, but recent studies have shown that its enzymes maybe considered as potential drug target. The purpose of this work was to identify the binding site in-silico and present the 3D structure of COVID-19 main-protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library. The obtained homotrimer oligo-state model was verified for reliability using PROCHECK, Verify3D, MolProbity and QMEAN. HHBlits software was used to determine structures that matched the target sequence by evolution. Structure quality verification through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the Mpro model. The features of the secondary structure indicated that the structure contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. The result of this study suggests that blocking expression of this protein may constitute an efficient approach for infection transmission blockage.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	07 Jul 2021 14:35
Last Modified:	07 Jul 2021 14:35
URI:	https://eprints.lmu.edu.ng/id/eprint/3233

Actions (login required)

View Item