YoyoMut: Interactive Exploration of SARS-CoV-2 Mutation Fixation and Reversion Through Time.

Area of Science:

• Virology
• Genomics
• Bioinformatics

Background:

• Viral evolution frequently involves reversion of amino acid mutations in structural proteins.
• The SARS-CoV-2 pandemic has generated a vast dataset of whole genome sequences, enabling ecological studies.
• Understanding mutation dynamics is crucial for developing effective countermeasures.

Purpose of the Study:

• To introduce YoyoMut, a tool for automated scanning and reporting of cyclic and reverting mutations in SARS-CoV-2.
• To visualize mutation trends and identify patterns in protein evolution, particularly within the Spike protein.
• To support immunologists and structural biologists in their research and development efforts.

Main Methods:

• Regular scanning of open SARS-CoV-2 genomic data using the YoyoMut tool.
• Reporting on cyclic and reverting mutations across all viral proteins, with a focus on Spike.
• Utilizing fine-grained trend visualization to distinguish mutated from non-mutated positions.
• Employing alternative algorithms for mutation classification (threshold-based or slope inversion).
• Analyzing 3D protein structures to identify spatial clustering of mutated sites.

Main Results:

• Identification of approximately 100 reverting and 50 fixated mutations within the Spike protein in the CoVSpectrum database.
• Demonstration of YoyoMut's capability to detect and classify various mutation patterns.
• Spatial clustering of adjacent mutated positions was identified using 3D protein structures.

Conclusions:

• Automated monitoring of mutation behaviors, including cyclic changes and reversions, aids scientific curation.
• YoyoMut provides informative reports that support daily research activities.
• Prioritizing the analysis of cyclic mutation and reversion models can inform the design of vaccines and anti-Spike monoclonal antibodies, potentially avoiding past development failures.