Revealing the Threat of Emerging SARS-CoV-2 Mutations to Antibody Therapies

Area of Science:

• Virology and Immunology
• Computational Biology
• Genomic Epidemiology

Background:

• The COVID-19 pandemic necessitates ongoing vaccine and therapeutic development.
• Emerging SARS-CoV-2 variants pose a significant threat to existing vaccines and monoclonal antibody (mAb) therapies.
• Limited experimental data exists on the impact of numerous SARS-CoV-2 mutations on a wide range of clinical-stage mAbs.

Purpose of the Study:

• To investigate the impact of frequently occurring mutations in the SARS-CoV-2 spike protein receptor-binding domain (RBD) on 16 mAbs in clinical trials.
• To develop and validate a deep learning strategy for predicting mutation impacts on mAb efficacy.
• To identify specific mutations that may compromise the effectiveness of current and developing antibody therapies.

Main Methods:

• Development of a topology-based deep learning strategy.
• Validation of the model using tens of thousands of experimental data points.
• Analysis of 796,759 SARS-CoV-2 genome isolates to identify 606 non-degenerate RBD mutations.

Main Results:

• The study identified 606 non-degenerate RBD mutations and analyzed their impact on 16 clinical-stage mAbs.
• Findings align with experimental data for known variants (Alpha, Beta, Gamma, Delta, Epsilon, Kappa).
• Specific high-frequency mutations (e.g., R346K/S, N439K, F486L) are predicted to compromise several mAbs in clinical trials.

Conclusions:

• A deep learning approach effectively predicts the impact of SARS-CoV-2 mutations on mAb efficacy.
• Identified mutations pose a potential threat to multiple mAbs from various developers, including those in clinical trials.
• The study provides a broader understanding of how viral mutations can affect the efficacy of current vaccines and antibody treatments.