High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

Angermueller, C.; Marie, Z.; Jester, B.; Engelhart, E.; Emerson, R.; Alipanahi, B.; McCaw, Z. R.; Roberts, J.; Lopez, R. M.; Younger, D.; Colwell, L.

doi:10.1101/2023.12.01.569227

bioRxiv · 10.1101/2023.12.01.569227

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

Angermueller, C.·Marie, Z.·Jester, B.·Engelhart, E.·Emerson, R.·Alipanahi, B.·McCaw, Z. R.·Roberts, J.·Lopez, R. M.·Younger, D.·Colwell, L.

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Abstract

Treating rapidly evolving pathogenic diseases such as COVID-19 requires a therapeutic approach that accommodates the emergence of viral variants over time. Our machine learning (ML)-guided sequence design platform combines high-throughput experiments with ML to generate highly diverse single-domain antibodies (VHHs) that bind and neutralize SARS-CoV-1 and SARS-CoV-2. Crucially, the model, trained using binding data against early SARS-CoV variants, accurately captures the relationship between VHH sequence and binding activity across a broad swathe of sequence space. We discover ML-designed VHHs that exhibit considerable cross-reactivity and successfully neutralize targets not seen during training, including the Delta and Omicron BA.1 variants of SARS-CoV-2. Our ML-designed VHHs include thousands of variants 4-15 mutations from the parent sequence with significantly improved activity, demonstrating that ML-guided sequence design can successfully navigate vast regions of sequence space to unlock and future-proof potential therapeutics against rapidly evolving pathogens.

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Angermueller, C., Marie, Z., Jester, B., Engelhart, E., Emerson, R., Alipanahi, B., McCaw, Z. R., Roberts, J., Lopez, R. M., Younger, D., Colwell, L.. 2023-12-01. High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2. https://doi.org/10.1101/2023.12.01.569227

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