SARS-CoV-2: lessons in virus mutation prediction and pandemic preparedness

Area of Science:

• Virology
• Computational Biology
• Epidemiology

Background:

• The COVID-19 pandemic highlighted the urgent need for effective viral variant monitoring.
• Variant emergence poses significant threats, particularly concerning vaccine escape and disease spread.

Purpose of the Study:

• To categorize and evaluate methods for predicting viral mutations.
• To emphasize the advantages of reverse mutation prediction strategies for anticipating viral evolution.

Main Methods:

• Classification of mutation prediction methods into forward and reverse approaches.
• Leveraging deep mutational scanning and machine learning for reverse mutation prediction.
• Analyzing the potential of computational tools for predicting pathogen genetic profiles and phenotypes.

Main Results:

• Reverse mutation prediction allows sampling of a more complete evolutionary space, including non-existent sequences.
• Advancements in deep mutational scanning have generated substantial data for machine learning models.
• Computational tools utilizing these strategies show improved prediction accuracy.

Conclusions:

• Reverse mutation prediction strategies offer a robust framework for anticipating viral evolution.
• Integrated prediction approaches are essential for enhancing pandemic preparedness and response.
• These methods provide generalizable opportunities for proactive viral surveillance.