Cellular Automata Model for Non-Structural Proteins Comparing Transmissibility and Pathogenesis of SARS Covid (CoV-2, CoV) and MERS Covid

TL;DR

This study uses a Cellular Automata enhanced Machine Learning model to compare the structure-function deviations of non-structural proteins in SARS-CoV-2, SARS-CoV, and MERS-CoV, revealing insights into their transmissibility and pathogenic differences.

Contribution

It introduces a novel CAML model for analyzing amino acid chains of viral nsps and maps these parameters to experimental features, advancing computational virology methods.

Findings

Higher transmissibility of SARS-CoV-2 compared to SARS-CoV for major nsps.

Deviations of MERS-CoV from SARS-CoV in virulence and pathogenesis.

A machine learning framework linking model parameters to experimental data.

Abstract

Significantly higher transmissibility of SARS CoV-2 (2019) compared to SARS CoV (2003) can be attributed to mutations of structural proteins (Spike S, Nucleocapsid N, Membrane M, and Envelope E) and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly and shedding. The non-structural proteins (nsps) avail host protein synthesis machinery to initiate viral replication, along with neutralization of host immune defense. The key protein out of the 16 nsps, is the non-structural protein nsp1, also known as the leader protein. Nsp1 leads the process of hijacking host resources by blocking host translation. This paper concentrates on the analysis of nsps of SARS covid (CoV-2, CoV) and MERS covid based on Cellular Automata enhanced Machine Learning (CAML) model developed for study of biological strings. This computational model compares deviation of structure - function of CoV-2 from that of CoV employing CAML model parameters derived out of CA evolution of amino acid chains of nsps. This comparative analysis points to - (i) higher transmissibility of CoV-2 compared to CoV for major nsps, and (ii) deviation of MERS covid from SARS CoV in respect of virulence and pathogenesis. A Machine Learning (ML) framework has been designed to map the CAML model parameters to the physical domain features reported in in-vitro/in-vivo/in-silico experimental studies. The ML framework enables us to learn the permissible range of model parameters derived out of mutational study of sixteen nsps of three viruses.