Post by Nadica (She/Her) on Dec 9, 2024 4:24:53 GMT
An evolutionary theory on virus mutation in COVID-19 - Published March 21, 2024
Highlights
•Virus variants are represented by 4-letter sequence based on AA mutations on spike.
•An n-distance algorithm in UPGMA is utilized to derive variant phylogenetic tree.
•Discovering new strain involves union of mutated sites and randomly generated set X.
•New macro-lineage of SARS-CoV-2 is predicted when X reaches a sufficient scale.
•Demarcation values that differentiate between various macro-lineages are derived.
Abstract
With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.
Highlights
•Virus variants are represented by 4-letter sequence based on AA mutations on spike.
•An n-distance algorithm in UPGMA is utilized to derive variant phylogenetic tree.
•Discovering new strain involves union of mutated sites and randomly generated set X.
•New macro-lineage of SARS-CoV-2 is predicted when X reaches a sufficient scale.
•Demarcation values that differentiate between various macro-lineages are derived.
Abstract
With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.