Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2

5 Mar 2025

Abstract excerpt

Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). However, Omicron and its sub-lineages, as well as other heavily mutated variants, have rendered many neutralizing antibodies ineffective. Here we show that antibodies with enhanced resistance to the evolution of SARS-CoV-2 can be identified via deep mutational...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Monoclonal and Polyclonal Antibodies Research
Share to SARS-CoV-2 and COVID-19 Research
Share to vaccines and immunoinformatics approaches

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40044817
DOI: 10.1038/s41551-025-01353-4

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2PMID 40044817Read ↗

Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variantsSemantic proximity Read ↗

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.Semantic proximity Read ↗

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2Semantic proximity Read ↗

Predictive profiling of SARS-CoV-2 variants by deep mutational learningSemantic proximity Read ↗

Preemptive optimization of a clinical antibody for broad neutralization of SARS-CoV-2 variants and robustness against viral escapeSemantic proximity Read ↗

Evaluating SARS-CoV-2 Antibody Resilience via Prediction and Design of Escape Viral VariantsSemantic proximity Read ↗

Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape.Semantic proximity Read ↗

Machine Learning Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning ExperimentsSemantic proximity Read ↗

Revealing the threat of emerging SARS-CoV-2 mutations to antibody therapiesSemantic proximity Read ↗

Revealing the Threat of Emerging SARS-CoV-2 Mutations to Antibody Therapies.Semantic proximity Read ↗

Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Semantic proximity Read ↗

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariantsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.