Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model
Raju Hazari, P Pal Chaudhuri

TL;DR
This study uses cellular automata models to analyze how mutations in the coronavirus envelope protein influence its structure, function, and interaction with host cells, potentially affecting transmissibility.
Contribution
It introduces a novel computational cellular automata model to compare wild and mutant envelope proteins of SARS-CoV-2 and other coronaviruses, linking mutations to structural and functional differences.
Findings
Mutations in envelope protein correlate with changes in structure and function.
The model distinguishes differences between wild and mutant proteins.
Mutational impacts on transmissibility are inferred from structural analysis.
Abstract
The reason of significantly higher transmissibility of SARS Covid (2019 CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be attributed to mutations reported in structural proteins, and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly, and shedding. Envelope protein E is one of the four structural proteins of minimum length. Recent studies have confirmed critical role played by the envelope protein in the viral life cycle including assembly of virion exported from infected cell for its transmission. However, the determinants of the highly complex viral - host interactions of envelope protein, particularly with host Golgi complex, have not been adequately characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids differ in four amino acid locations. The additional amino acid Gly (G)…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBacteriophages and microbial interactions · Fractal and DNA sequence analysis · Machine Learning in Bioinformatics
