ArticleNature communications2022
An in silico method to assess antibody fragment polyreactivity.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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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
39 citing papers in PubMed.
- Article
- Prediction of antibody non-specificity using protein language models and biophysical parameters.mAbs · 2026Article
- Efficient inference of non-polyreactive antibody variants dependent on local fine-tuning.mAbs · 2026Article
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- Profiling Protein-Peptide Interactions by Yeast Surface Display.Current protocols · 2026Article
- A Simple and Cost-Effective Electrochemical Assay for Heart Failure Prognosis Using Soluble Suppression of Tumorigenicity 2‑Specific Nanobodies.ACS pharmacology & translational science · 2026Article
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Mechanistic insight into the oxidative degradation of monoclonal antibodies: relevance to developability and the design of stable pharmaceutical formulations.Biochemical Society transactions · 2026Review
- In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer.Nature communications · 2026Article
- Article
- Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis.bioRxiv : the preprint server for biology · 2026Article
- Modified protein selection strategy based on Escherichia coli's Hitchhiker transport and validation through selection of nanobodies targeting bovine interferon gamma.Scientific reports · 2026Article
- Machine learning enables efficient and effective affinity maturation of nanobodies.bioRxiv : the preprint server for biology · 2026Article
- Engineering Single-Chain Antibody Fragment (scFv) Variants Targeting A Disintegrin and Metalloproteinase-17 (ADAM-17).Biomolecules · 2025Article
- Article
- Article
- Antibody Polyreactivity: A Challenger of Immune Paradigms.Immunology · 2025Review
- Review
- Enhancing polyreactivity prediction of preclinical antibodies through fine-tuned protein language models.Journal of pharmaceutical analysis · 2025Article
- Fifty years of monoclonals: the past, present and future of antibody therapeutics.Nature reviews. Immunology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.
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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.