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Learning from pre-pandemic data to forecast viral antibody escape

View ORCID ProfileNicole N. Thadani, View ORCID ProfileSarah Gurev, View ORCID ProfilePascal Notin, View ORCID ProfileNoor Youssef, Nathan J. Rollins, View ORCID ProfileChris Sander, View ORCID ProfileYarin Gal, View ORCID ProfileDebora S. Marks
doi: https://doi.org/10.1101/2022.07.21.501023
Nicole N. Thadani
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Sarah Gurev
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
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Pascal Notin
3OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
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Noor Youssef
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Nathan J. Rollins
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Chris Sander
4Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
5Department of Cell Biology, Harvard Medical School, Boston, MA, USA
6Broad Institute of Harvard and MIT, Cambridge, MA, USA
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Yarin Gal
3OATML Group, Department of Computer Science, University of Oxford, Oxford, UK
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Debora S. Marks
1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
6Broad Institute of Harvard and MIT, Cambridge, MA, USA
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  • For correspondence: debbie@hms.harvard.edu
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Abstract

From early detection of variants of concern to vaccine and therapeutic design, pandemic preparedness depends on identifying viral mutations that escape the response of the host immune system. While experimental scans are useful for quantifying escape potential, they remain laborious and impractical for exploring the combinatorial space of mutations. Here we introduce a biologically grounded model to quantify the viral escape potential of mutations at scale. Our method - EVEscape - brings together fitness predictions from evolutionary models, structure-based features that assess antibody binding potential, and distances between mutated and wild-type residues. Unlike other models that predict variants of concern based on newly observed variants, EVEscape has no reliance on recent community prevalence, and is applicable before surveillance sequencing or experimental scans are broadly available. We validate EVEscape predictions against experimental data on H1N1, HIV and SARS-CoV-2, including data on immune escape. For SARS-CoV-2, we show that EVEscape anticipates mutation frequency, strain prevalence, and escape mutations. Drawing from GISAID, we provide continually updated escape predictions for all current strains of SARS-CoV-2.

Competing Interest Statement

D.S.M. is an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic and Genentech, and is a co-founder of Seismic Therapeutic.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 22, 2022.
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Learning from pre-pandemic data to forecast viral antibody escape
Nicole N. Thadani, Sarah Gurev, Pascal Notin, Noor Youssef, Nathan J. Rollins, Chris Sander, Yarin Gal, Debora S. Marks
bioRxiv 2022.07.21.501023; doi: https://doi.org/10.1101/2022.07.21.501023
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Learning from pre-pandemic data to forecast viral antibody escape
Nicole N. Thadani, Sarah Gurev, Pascal Notin, Noor Youssef, Nathan J. Rollins, Chris Sander, Yarin Gal, Debora S. Marks
bioRxiv 2022.07.21.501023; doi: https://doi.org/10.1101/2022.07.21.501023

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