Modeling the Mutational Effects on Biochemical Phenotypes of SARS-CoV-2 Using Molecular Fields

Area of Science:

• Virology
• Computational Biology
• Biochemistry

Background:

• SARS-CoV-2 variants evolve with mutations in the spike protein's receptor-binding domain (RBD).
• These mutations enhance transmissibility, pathogenicity, and immune evasion by altering interactions with human ACE2 (hACE2) and antibodies.
• Predicting the functional impact of RBD mutations is vital for public health risk assessment.

Purpose of the Study:

• To develop and validate a quantitative framework for predicting the biochemical phenotypes of SARS-CoV-2 RBD variants.
• To assess the impact of mutations on hACE2 binding affinity and antibody neutralization escape.
• To provide a tool for early risk assessment of emerging SARS-CoV-2 variants.

Main Methods:

• Application of a Mutation-dependent Biomacromolecular Quantitative Structure-Activity Relationship (MB-QSAR) framework.
• Training models on comprehensive deep mutational scanning (DMS) datasets.
• Validation of predictive performance for hACE2 binding and antibody neutralization.

Main Results:

• MB-QSAR models achieved high predictive accuracy (r² > 0.8 for hACE2 binding, r² > 0.7 for antibody escape).
• The framework demonstrated strong generalization to multi-mutant variants and current SARS-CoV-2 lineages.
• Structural analysis provided mechanistic insights into RBD-ACE2 and RBD-antibody interactions.

Conclusions:

• MB-QSAR is a rapid, accurate, and interpretable tool for predicting protein-protein interactions and viral adaptation.
• This approach facilitates early risk assessment of novel SARS-CoV-2 variants.
• The findings support the rational design of broadly protective vaccines and therapeutics.