ArticleCell2022
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.
Article in Cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
What it found
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Who cites it
66 citing papers in PubMed, 114 citations in OpenAlex.
- Serum Escape Landscape of SARS-CoV-2 Omicron JN.1 and XEC RBD Under COVID-19 Vaccine Breakthrough Immunity in China.Microorganisms · 2026Article
- Concepts of RNA virus evolution for the design of better antiviral countermeasures.Nature reviews. Microbiology · 2026Review
- A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal resolution.Nature microbiology · 2026Article
- Dissecting serum polyclonal antibody escape to SARS-CoV-2 variants by deep mutational learning.Cell reports methods · 2026Article
- Deep mutational scanning of recent SARS-CoV-2 variants highlights changing amino acid preferences within epistatic hotspot residues.PLoS pathogens · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- SARS-CoV-2 wastewater genomic surveillance: approaches, challenges, and opportunities.Genome biology · 2026Review
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Determinants of success and failure of antibody-based strategies against respiratory viruses: insights from RSV and SARS-CoV-2.Frontiers in immunology · 2026Review
- Droplet-based single-cell pairing for high-throughput interaction mapping of antigen-receptor combinations.Science advances · 2025Article
- LL-37 Inhibits TMPRSS2-Mediated S2' Site Cleavage and SARS-CoV-2 Infection but Not Omicron Variants.Cell proliferation · 2025Article
- Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface.bioRxiv : the preprint server for biology · 2025Article
- PLMFit: benchmarking transfer learning with protein language models for protein engineering.Briefings in bioinformatics · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.Briefings in bioinformatics · 2025Article
- Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.Nature biomedical engineering · 2025Article
- Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights.Biophysical reviews · 2025Review
- Unleashing the potential of artificial intelligence in infectious diseases.National science review · 2025Article
- Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
6 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
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.
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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.