Selected article for: "cell cell and virulence factor"
Author: Chaudhuri, Ankur
Title: Comparative analysis of non structural protein 1 of SARS-CoV2 with SARS-CoV1 and MERS-CoV: An in silico study
  • Cord-id: jkqk2xjd
  • Document date: 2021_6_9
    • ID: jkqk2xjd
    Snippet: The recently emerged SARS-CoV2 caused a major pandemic of coronavirus disease (COVID-19). Non structural protein 1 (nsp1) is found in all beta coronavirus that cause severe respiratory disease. This protein is considered as a virulence factor and has an important role in pathogenesis. This study aims to elucidate the structural conformations of nsp1 to aid in the prediction of epitope sites and identification of important residues for targeted therapy against COVID-19. In this study, molecular m
    Document: The recently emerged SARS-CoV2 caused a major pandemic of coronavirus disease (COVID-19). Non structural protein 1 (nsp1) is found in all beta coronavirus that cause severe respiratory disease. This protein is considered as a virulence factor and has an important role in pathogenesis. This study aims to elucidate the structural conformations of nsp1 to aid in the prediction of epitope sites and identification of important residues for targeted therapy against COVID-19. In this study, molecular modelling coupled with molecular dynamics simulations were performed to analyse the conformational landscape of nsp1 homologs of SARS-CoV1, SARS-CoV2 and MERS-CoV. Principal component analysis escorted by free energy landscape revealed that SARS-CoV2 nsp1 protein shows greater flexibility compared to SARS-CoV1 and MERS-CoV nsp1. Sequence comparison reveals that 28 mutations are present in SARS-CoV2 nsp1 protein compared to SARS-CoV1 nsp1. Several B-cell and T-cell epitopes were identified by an immunoinformatics approach. SARS-CoV2 nsp1 protein binds with the interface region of the palm and finger domain of POLA1 via hydrogen bonding and salt bridge interactions. Taken together, these in silico findings may help in the development of therapeutics specific against COVID-19.

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