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

## TL;DR

This study uses a new method to predict how mutations in SARS-CoV-2 affect its ability to bind to human cells and evade antibodies, helping assess the threat of new variants.

## Contribution

The study introduces the MB-QSAR framework for modeling mutational effects on SARS-CoV-2 biochemical phenotypes with high accuracy.

## Key findings

- MB-QSAR models achieved r2 > 0.8 for hACE2 binding affinity and r2 > 0.7 for antibody neutralization escape.
- The method generalizes well to multi-mutant variants and circulating SARS-CoV-2 lineages.
- Model-derived interaction profiles provide insights into RBD–ACE2 and RBD–antibody interfaces.

## Abstract

The ongoing evolution of SARS-CoV-2 has given rise to variants with enhanced transmissibility and pathogenicity, many of which harbor mutations in the receptor-binding domain (RBD) of the viral spike protein. These mutations often confer increased viral fitness and immune evasion by modulating interactions with the human ACE2 receptor (hACE2) and escaping neutralizing antibodies. Accurate prediction of the functional consequences of such mutations—particularly their effects on receptor binding and antibody escape—is critical for assessing the public health threat posed by emerging variants. In this study, we apply a Mutation-dependent Biomacromolecular Quantitative Structure–Activity Relationship (MB-QSAR) framework to quantitatively model the biochemical phenotypes of RBD variants. Trained on comprehensive deep mutational scanning (DMS) datasets, our models exhibit strong predictive performance, achieving correlation coefficients (r2) exceeding 0.8 for hACE2 binding affinity and 0.7 for antibody neutralization escape. Importantly, the MB-QSAR approach generalizes well to multi-mutant variants and currently circulating lineages. Structural analysis based on model-derived interaction profiles offers mechanistic insights into key RBD–ACE2 and RBD–antibody interfaces, helping the rational design of broadly protective vaccines and therapeutics. This work establishes MB-QSAR as a rapid, accurate, and interpretable tool for the prediction of protein–protein interaction and forecasting viral adaptation, thereby facilitating early risk assessment of novel SARS-CoV-2 variants.