A deep learning approach predicting the activity of COVID-19 therapeutics and vaccines against emerging variants

Understanding which viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses like SARS-CoV-2. Yet, conventional assays are labor intensive and cannot capture the full spectrum of variants. We present a deep learning approach to...

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Bibliographic Details
Published in:NPJ systems biology and applications 2024-11, Vol.10 (1), p.138-10
Main Authors: Matson, Robert P., Comba, Isin Y., Silvert, Eli, Niesen, Michiel J. M., Murugadoss, Karthik, Patwardhan, Dhruti, Suratekar, Rohit, Goel, Elizabeth-Grace, Poelaert, Brittany J., Wan, Kanny K., Brimacombe, Kyle R., Venkatakrishnan, AJ, Soundararajan, Venky
Format: Article
Language:English
Subjects:
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Amino acids
Antibodies
Antibodies, Neutralizing - immunology
Antibodies, Viral - immunology
Bioinformatics
Biology
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
COVID-19
COVID-19 - immunology
COVID-19 - prevention & control
COVID-19 Drug Treatment
COVID-19 vaccines
COVID-19 Vaccines - immunology
Deep Learning
Humans
Influenza
Life Sciences
Mutation
Neural networks
Plasma
Proteins
SARS-CoV-2 - immunology
Severe acute respiratory syndrome coronavirus 2
Spike Glycoprotein, Coronavirus - genetics
Spike Glycoprotein, Coronavirus - immunology
Systems Biology
Vaccines
Viruses
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Summary:Understanding which viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses like SARS-CoV-2. Yet, conventional assays are labor intensive and cannot capture the full spectrum of variants. We present a deep learning approach to predict changes in neutralizing antibody activity of COVID-19 therapeutics and vaccine-elicited sera/plasma against emerging viral variants. Our approach leverages data of 67,885 unique SARS-CoV-2 Spike sequences and 7,069 in vitro assays. The resulting model accurately predicted fold changes in neutralizing activity (R 2  = 0.77) for a test set ( N  = 980) of data collected up to eight months after the training data. Next, the model was used to predict changes in activity of current therapeutic and vaccine-induced antibodies against emerging SARS-CoV-2 lineages. Consistent with other work, we found significantly reduced activity against newer XBB descendants, notably EG.5, FL.1.5.1, and XBB.1.16; primarily attributed to the F456L spike mutation.
ISSN:2056-7189
2056-7189
DOI:10.1038/s41540-024-00471-0