ArticleCommunications biology2023
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 papers.
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Who cites it
184 citing papers in PubMed, 287 citations in OpenAlex.
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- Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55.Proteins · 2026Article
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
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- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- Developability engineering of scFvs enables robust CAR function under transient mRNA expression.Scientific reports · 2026Article
- CD98hc-targeted antibody shuttles for central nervous system delivery with broad cross-species reactivity.Nature biomedical engineering · 2026Article
124 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies, (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89Å, a 0.55Å improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download ( https://github.com/oxpig/ImmuneBuilder ) and to use via our webserver ( http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred ). We also make available structural models for ~150 thousand non-redundant paired antibody sequences ( https://doi.org/10.5281/zenodo.7258553 ).
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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.