Bioinformatics techniques for efficient structure prediction of SARS-CoV-2 protein ORF7a via structure prediction approaches (preprint)

ABSTRACT

Protein is the building block for all organisms. Protein structure prediction is always a complicated task in the field of proteomics. DNA and protein databases can find the primary sequence of the peptide chain and even similar sequences in different proteins. Mainly, there are two methodologies based on the presence or absence of a template for Protein structure prediction. Template-based structure prediction (threading and homology modeling) and Template-free structure prediction (ab initio). Numerous web-based servers that either use templates or do not can help us forecast the structure of proteins. In this current study, ORF7a, a transmembrane protein of the SARS-coronavirus, is predicted using Phyre2, IntFOLD, and Robetta. The protein sequence is straightforwardly entered into the sequence bar on all three web servers. Their findings provided information on the domain, the region with the disorder, the global and local quality score, the predicted structure, and the estimated error plot. Our study presents the structural details of the SARS-CoV protein ORF7a. This immunomodulatory component binds to immune cells and induces severe inflammatory reactions.