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Deciphering the temporal and spatial mutation dynamics of the SARS-CoV-2 spike glycoprotein
Muhammad Hasan1, Shihong Chen1, Mengqi Jia1
1Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. kczhu@ust.hk.
Physical Chemistry Chemical Physics : PCCP
|June 16, 2026
Summary
We developed a statistical pipeline to track SARS-CoV-2 spike evolution. This analysis reveals increasing mutation rates and identifies key mutations, offering insights into viral evolution and variant emergence.
Area of Science:
- Virology
- Computational Biology
- Genomics
Background:
- The SARS-CoV-2 spike protein is crucial for viral entry and a primary target for vaccines and therapeutics.
- Understanding the evolutionary dynamics of the spike protein is essential for predicting viral adaptation and the emergence of new variants.
Purpose of the Study:
- To develop and apply a novel statistical pipeline for analyzing SARS-CoV-2 spike protein evolution.
- To identify evolutionarily significant mutations and understand the mechanisms driving spike glycoprotein adaptation.
Main Methods:
- A statistical pipeline integrating probability sequence density analysis and a composite metric for identifying leading mutations.
- The composite metric combines mutation eigenvector information with pairwise couplings for large-scale dataset analysis.
Main Results:
- Demonstrated a progressive increase in SARS-CoV-2 sequence mutation rates over time.
- Identified scaling behaviors predictive of variant emergence and evolutionary trends in spike mutation patterns.
- Characterized mechanisms of spike glycoprotein mutation acquisition.
Conclusions:
- The developed pipeline effectively analyzes SARS-CoV-2 spike evolution and identifies key mutations.
- Findings provide insights into the evolutionary dynamics of the spike protein and mechanisms of adaptation.
- This work aids in anticipating future viral evolution and informing public health strategies.
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