Predicting the mutational drivers of future SARS-CoV-2 variants of concern
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scitranslmed.abk3445.pdf
Description
Published version
Size
2.3 MB
Format
Adobe PDF
Checksum (MD5)
633d2b7312191197d53f02064148d596
Author(s)
Maher, M Cyrus
Bartha, Istvan
Weaver, Steven
di Iulio, Julia
Ferri, Elena
Soriaga, Leah
Lempp, Florian A
Hie, Brian L
Bryson, Bryan
Berger, Bonnie
Date Issued
2022
Journal
Science Translational Medicine
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Maher, M Cyrus, Bartha, Istvan, Weaver, Steven, di Iulio, Julia, Ferri, Elena et al. 2022. "Predicting the mutational drivers of future SARS-CoV-2 variants of concern." Science Translational Medicine, 14 (633).
Version
Final published version
Abstract
SARS-CoV-2 evolution threatens vaccine- and natural infection–derived immunity and the efficacy of therapeutic antibodies. To improve public health preparedness, we sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. We tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network–based protein sequence modeling and identified primary biological drivers of SARS-CoV-2 intrapandemic evolution. We found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. We retroactively identified with high accuracy (area under the receiver operator characteristic curve = 0.92 to 0.97) mutations that will spread, at up to 4 months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure where epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. We validated this result against Omicron, showing elevated predictive scores for its component mutations before emergence and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1126/SCITRANSLMED.ABK3445
