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Detection and classification of mutational field between Omicron BA.5 and other SARS-CoV-2 variants of concern with support vector machine

Kabin Kanjamapornkul^a, Thanyada Rungrotmongkol^b,c, Supot Hannongbua^a,*

 
ABSTRACT:     A theoretical investigation into the hidden source field of mutation affecting the curvature change in the spike (S) protein of Omicron BA.5 and related variants is reported. The curvature in the open string shape of S protein is defined using the Yang-Mills field over a new type of connection known as quantum genotype. By adding more invariant properties of curvature two-forms to the adaptive tensor fields in DNA, RNA, and protein molecules, we redefined the hidden quantum biological co-state of the mutational field between the virus and the host cell. The new algorithm was applied to classify mutations in S protein with a support vector machine. The results showed that the average performance of the prediction of unknown amino acids in 14 variants including Omicron BA.1?BA.5 is 97.79%. Additionally, we demonstrated a new approach for the quantitative measurement of changing curvature of S protein mutations in each amino acid. The empirical analysis of the probability distribution of time series data showed the evolution of the quantum genotype over time, revealing a new direction of evolution in SARS-CoV-2?s quantum genotype opposite to the period between 2020 and 2021. This work can be applied to detect new incoming novel variants of SARS-CoV-2 in the future and provide insight into the coupling between the passive and active sides of communication in the biomolecular layer

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* Corresponding author, E-mail: supot.h@chula.ac.th

Received 1 Sep 2022, Accepted 24 May 2023