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- Title
Predicting the mutational drivers of future SARS-CoV-2 variants of concern.
- Authors
Maher, M. Cyrus; Bartha, Istvan; Weaver, Steven; di Iulio, Julia; Ferri, Elena; Soriaga, Leah; Lempp, Florian A.; Hie, Brian L.; Bryson, Bryan; Berger, Bonnie; Robertson, David L.; Snell, Gyorgy; Corti, Davide; Virgin, Herbert W.; Kosakovsky Pond, Sergei L.; Telenti, Amalio
- 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. Forecasting SARS-CoV-2 mutation spread: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations that increase greatly in frequency are of potential importance for both antiviral immunity and the adoption of public health precautions. Maher et al. built a pipeline to predict which individual amino acid mutations in SARS-CoV-2 will become more prevalent over the coming months. This model looks at the changing prevalence of mutations averaged over the haplotypes on which they occur and can be applied to just the Spike protein or proteome-wide. The authors were able to validate their model by looking at the emergence of Omicron. This pipeline may help researchers forecast the driver mutations that may appear in future SARS-CoV-2 variants of concern.
- Subjects
COVID-19; PANDEMICS; SARS-CoV-2; NERVE tissue proteins
- Publication
Science Translational Medicine, 2022, Vol 14, Issue 633, p1
- ISSN
1946-6234
- Publication type
Article
- DOI
10.1126/scitranslmed.abk3445