Novel Entropy-Based Phylogenetic Algorithm: A New Approach for Classifying SARS-CoV-2 Variants

Area of Science:

• Virology
• Bioinformatics
• Genomics

Background:

• SARS-CoV-2 exhibits significant genetic diversity, necessitating robust classification of variants of concern (VOCs) and variants of interest (VOIs).
• Accurate and rapid identification of viral variants is crucial for effective public health response and surveillance.
• The spike protein (SP1) is a critical target due to its role in viral entry and immune evasion, and its high mutation rate.

Purpose of the Study:

• To develop a novel bioinformatics criterion for enhanced identification and classification of SARS-CoV-2 variants.
• To create a scalable and rapid method for analyzing large genomic datasets.
• To predict the impact of mutations on viral characteristics and potential risk.

Main Methods:

• Development of a unique phylogenetic algorithm.
• Calculation of electron-ion interaction potential (EIIP) entropy as a distance measure based on amino acid distribution in the spike protein (SP1).
• Application of the algorithm to large genomic datasets for variant analysis.

Main Results:

• The EIIP-entropy method provides a comprehensive and rapid approach for variant classification.
• The method effectively predicts the potential risk associated with emergent SARS-CoV-2 variants.
• Demonstrated scalability for analyzing extensive genomic data.

Conclusions:

• The novel EIIP-entropy bioinformatics criterion offers a robust tool for classifying SARS-CoV-2 variants into potential VOCs or VOIs.
• This approach can significantly augment global surveillance efforts and deepen the understanding of variant characteristics.
• The method holds potential for analyzing other emerging viral pathogens, enhancing global preparedness.