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Updated: Nov 8, 2025

Detection of SARS-CoV-2 Receptor-Binding Domain Antibody using a HiBiT-Based Bioreporter
Published on: August 12, 2021
Revealing the threat of emerging SARS-CoV-2 mutations to antibody therapies
Abstract:
The ongoing massive vaccination and the development of effective intervention offer the long-awaited hope to end the global rage of the COVID-19 pandemic. However, the rapidly growing SARS-CoV-2 variants might compromise existing vaccines and monoclonal antibody (mAb) therapies. Although there are valuable experimental studies about the potential threats from emerging variants, the results are limited to a handful of mutations and Eli Lilly and Regeneron mAbs. The potential threats from frequently occurring mutations on the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) to many mAbs in clinical trials are largely unknown. We fill the gap by developing a topology-based deep learning strategy that is validated with tens of thousands of experimental data points. We analyze 261,348 genome isolates from patients to identify 514 non-degenerate RBD mutations and investigate their impacts on 16 mAbs in clinical trials. Our findings, which are highly consistent with existing experimental results about variants from the UK, South Africa, Brazil, US-California, and Mexico shed light on potential threats of 95 high-frequency mutations to mAbs not only from Eli Lilly and Regeneron but also from Celltrion and Rockefeller University that are in clinical trials. We unveil, for the first time, that high-frequency mutations R346K/S, N439K, G446V, L455F, V483F/A, E484Q/V/A/G/D, F486L, F490L/V/S, Q493L, and S494P/L might compromise some of mAbs in clinical trials. Our study gives rise to a general perspective about how mutations will affect current vaccines.
Insights
New deep learning identifies high-frequency SARS-CoV-2 mutations threatening COVID-19 vaccines and monoclonal antibody therapies. This research predicts variant impacts on 16 clinical trial antibodies, offering crucial insights for future pandemic preparedness.
Area of Science:
- Virology and Immunology
- Computational Biology and Bioinformatics
- Drug Development and Therapeutics
Background:
- The emergence of SARS-CoV-2 variants poses a significant threat to the efficacy of existing COVID-19 vaccines and monoclonal antibody (mAb) therapies.
- Current research on variant threats is limited, focusing on a small number of mutations and specific mAbs, leaving a knowledge gap regarding broader impacts.
Approach:
- Developed a topology-based deep learning strategy, validated with extensive experimental data.
- Analyzed 261,348 SARS-CoV-2 genome isolates to identify 514 non-degenerate spike protein receptor-binding domain (RBD) mutations.
- Investigated the impact of these mutations on 16 different mAbs undergoing clinical trials.
Key Points:
- Findings align with experimental data on known variants (UK, South Africa, Brazil, US-California, Mexico).
- Identified 95 high-frequency mutations with potential to compromise mAbs from multiple developers (Eli Lilly, Regeneron, Celltrion, Rockefeller University).
- Highlighted specific mutations (e.g., E484Q/V/A/G/D, F486L, F490L/V/S) that may reduce mAb effectiveness.
Conclusions:
- Provides a comprehensive analysis of mutation impacts on a wide range of clinical trial mAbs.
- Offers critical insights into how SARS-CoV-2 mutations can affect current vaccine and therapeutic strategies.
- Establishes a predictive framework for assessing future variant threats to COVID-19 interventions.
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