Bioinformatics techniques for efficient structure prediction of SARS-CoV-2 protein ORF7a via structure prediction approaches
Kazmi, A.; Kazim, M.; Aslam, F.; Kazmi, S. M.-u.-H.; Wahab, A.; Mikhlef, R. M.; Khizar, C.; Kazmi, A.; Wazir, N. U.; Mainali, R. P.
Show 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.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sequence alignment using machine learning for accurate template-based protein structure prediction 97%
- PICKLUSTER: A protein-interface clustering and analysis plug-in for UCSF ChimeraX 96%
- A de novo protein structure prediction by iterative partition sampling, topology adjustment, and residue-level distance deviation optimization 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.