Learning the language of viral evolution and escape
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Abstract
Natural language predicts viral escape: Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. 284 ; see also p. 233
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The B.1.617 lineage, also known as the Delta variant, contains two mutations (L452R and T478K) in the RBD that facilitate viral escape.
“For example, the B.1.617 lineage, also known as the Delta variant, contains two mutations (L452R and T478K) in the RBD that facilitate viral escape—the ability of viruses to evade the immune system and cause disease (22).”
From Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (2022) · cites this paper as [22]
The L452R mutation is located at the periphery of the receptor binding motif (RBM) and reduces neutralizing activity by antibodies.
“The L452R mutation is located at the periphery of the receptor binding motif (RBM) and is found to reduce neutralizing activity by antibodies.”
From Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (2022) · cites this paper as [22]
The T478K mutation in the RBD, located within the epitope region in the RBM, is associated with antibody escape.
“The T478K mutation in the RBD, located within the epitope region in the RBM, is also associated with antibody escape.”
From Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (2022) · cites this paper as [22]
When trained on tens of thousands of evolutionarily related proteins, the learned probability mass function describing the empirical distribution over naturally occurring sequences has shown promise for predicting the fitness of sequence variants.
“When trained on tens of thousands of evolutionarily related proteins, the learned probability mass function describing the empirical distribution over naturally occurring sequences has shown promise for predicting the fitness of sequence variants 29-31.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [31]
Language models learn the probability of a sequence occurring and this can be directly applied to predict the fitness of sequence mutations.
“Language models learn the probability of a sequence occurring and this can be directly applied to predict the fitness of sequence mutations 29-31 .”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [31]
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- Authors
- Hie, B., Zhong, E. D., Berger, B., & Bryson, B.
- Affiliations
- Ragon Institute of MGH, MIT and Harvard (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Berger, Bonnie
- Bryson, Bryan
- Venue
- Science
- Published
- 2021-01-15
- Volume
- 371
- Issue
- 6526
- Pages
- 284-288
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3121000782
- Cited by
- 361
- References
- 73
- Citations by year
- 2026: 72 · 2025: 66 · 2024: 70 · 2023: 61 · 2022: 57 · 2021: 35
- FWCI
- 17.72
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (Graduate Research Fellowship)
- National Institutes of Health (R01 GM081871)
- U.S. Department of Defense (National Defense Science and Engineering Graduate Fellowship (NDSEG))
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- viral immune evasion, immune evasion, escape mutations, viral evolution, influenza hemagglutinin, HIV-1 envelope glycoprotein, SARS-CoV-2 spike protein, language models, machine learning, viral infectivity, vaccine design
- MeSH terms
- Protein Domains, COVID-19, SARS-CoV-2, Acquired Immunodeficiency Syndrome, Binding Sites, Humans, Influenza, Human, Mutation, Influenza A virus, HIV-1, Evolution, Molecular, Hemagglutinin Glycoproteins, Influenza Virus, env Gene Products, Human Immunodeficiency Virus, Spike Glycoprotein, Coronavirus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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