Multiple-Variable Semi-Covariance Virus Protease Analysis

Version 1.0.0 (806 ko) par steed huang
Multiple-Variable Semi-Covariance Co-Efficiency Analysis of RNA-dependent RNA polymerase Proteins from SARS-CoV-2 variant and animals'.
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Mise à jour 24 juin 2023

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Most small molecule antiviral drugs focus on two targets, 3CL protease and RdRp protease. This classifies its drugs into two categories: 3CL protease inhibitors and RdRp analogs. As a prodrug of ribonucleoside analogs, the mechanism of action of the RdRp target drug is not to directly eliminate or inhibit the virus, but to "trick" the virus. Misreplication (mutation), resulting in the synthesis of non-infectious pseudoviruses. The mechanism of action of the 3CL target is more direct. It is a peptidomimetic inhibitor of the main protease Mpro of the new coronavirus, in the human body. In order to fully understand RdRp evolution path (including cross-species characterization change) and implicitation for future virus variants. Traditional protein modelings treat mass, charge, GRAVY, PH values separately, in this study we combine four of them into one index, to explain a group statistical phenomenon with positive and negative fluctuations of amino-acid multiple-variable semi-covariance co-efficiency in RdRp proteins from coronaviruses, we propose group-based equivalent Mass-Charge modeling. The number of amino acids, amino-acid composition, charges, molecular weight, isoelectric point, hydropathicity, and mass-charge ratio of the proteins were taken into consideration. RdRp proteins from SARS-CoV-2 variants, seasonal, bat, murine etc coronaviruses were analyzed. The analyses with the algorithm provide insights of evolving trends of the viral proteins and demonstrate that the Mass-Charge covariance co-efficiency can distinguish subtle differences between biological properties of RdRp proteins and correlate well with viral physiochemical properties and phylogenetic relationship. This modeling may also be used in analyzing other proteins from pathogens.

Citation pour cette source

steed huang (2024). Multiple-Variable Semi-Covariance Virus Protease Analysis (https://www.mathworks.com/matlabcentral/fileexchange/131563-multiple-variable-semi-covariance-virus-protease-analysis), MATLAB Central File Exchange. Récupéré le .

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1.0.0