ArticleScience translational medicine2022
Predicting the mutational drivers of future SARS-CoV-2 variants of concern.
Article in Science translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
115 citing papers in PubMed, 1 synthesis or guideline pooled it, 188 citations in OpenAlex.
- Duration of immunity to SARS-CoV-2 in children after natural infection or vaccination in the omicron and pre-omicron era: A systematic review of clinical and immunological studies.Frontiers in immunology · 2022Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- Scanning the horizon: deep mutational scanning approaches in virology.Journal of virology · 2026Review
- A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal resolution.Nature microbiology · 2026Article
- Deep mutational scanning of recent SARS-CoV-2 variants highlights changing amino acid preferences within epistatic hotspot residues.PLoS pathogens · 2026Article
- Utilizing virus genomic surveillance to predict vaccine effectiveness.PLoS computational biology · 2026Article
- Constrained evolutionary funnels shape viral immune escape.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Constrained Diffusion as a Paradigm for Evolution.bioRxiv : the preprint server for biology · 2026Article
- Potent efficacy of an NA-targeting antibody against a broad spectrum of H5N1 influenza viruses.Nature communications · 2026Article
- Predictive and interpretable machine learning for COVID-19 resurgences: the role of SARS-CoV-2 variants in the post-pandemic era.BMC infectious diseases · 2025Article
- Development and characterization of a bivalent mRNA vaccine targeting the Delta and Omicron variants of SARS-CoV-2.Human vaccines & immunotherapeutics · 2025Article
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Dynamic Allostery: Evolution's Double-Edged Sword in Protein Function and Disease.Journal of molecular biology · 2025Review
- Predicting the trend of SARS-CoV-2 mutation frequencies using historical data.Bioinformatics (Oxford, England) · 2025Article
- In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest.Nature communications · 2025Article
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.Briefings in bioinformatics · 2025Article
- Predicting high-fitness viral protein variants with Bayesian active learning and biophysics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- SARS-CoV-2: The Interplay Between Evolution and Host Immunity.Annual review of immunology · 2025Review
- Few-Shot Viral Variant Detection via Bayesian Active Learning and Biophysics.bioRxiv : the preprint server for biology · 2025Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.