Predicting the mutational drivers of future SARS-CoV-2 variants of concern.
M Cyrus Maher, Istvan Bartha, Steven Weaver, Julia di Iulio, Elena Ferri, Leah Soriaga, Florian A Lempp, Brian L Hie, Bryan Bryson, Bonnie Berger, David L Robertson, Gyorgy Snell, Davide Corti, Herbert W Virgin, Sergei L Kosakovsky Pond, Amalio Telenti
Author Information
M Cyrus Maher: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Istvan Bartha: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Steven Weaver: Department of Biology, Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA. ORCID
Julia di Iulio: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Elena Ferri: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Leah Soriaga: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Florian A Lempp: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Brian L Hie: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. ORCID
Bryan Bryson: Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA. ORCID
Bonnie Berger: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. ORCID
David L Robertson: MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow GS1 1QH, UK. ORCID
Gyorgy Snell: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Davide Corti: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Herbert W Virgin: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
Sergei L Kosakovsky Pond: Department of Biology, Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA. ORCID
Amalio Telenti: Vir Biotechnology, San Francisco, CA 94158, USA. ORCID
SARS-CoV-2 evolution threatens vaccine- and natural infection-derived immunity as well as 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 intra-pandemic 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, AUROC=0.92-0.97) mutations that will spread, at up to four months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure wherein 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 validate this result against Omicron, showing elevated predictive scores for its component mutations prior to 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.
References
Cell. 2021 May 27;184(11):2939-2954.e9
[PMID: 33852911]
