Predicting the conformational flexibility of antibody and T-cell receptor CDRs
Open accessPreprint — published as 10.1038/s42256-025-01131-6 · Paper page
Abstract
Many proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. We can now computationally predict a single, static protein structure with high accuracy. However, we are not yet able to reliably predict structural flexibility. A major factor limiting such predictions is the scarcity of suitable training data. Here, we focus on predicting the structural flexibility of the functionally important antibody and T-cell receptor CDR3 loops. We extracted a dataset of CDR3 like loop motifs from the PDB to create ALL-conformations, a dataset containing 1.2 million structures and more than 100,000 unique sequences. Using this dataset, we develop ITsFlexible a method classifying CDR3 flexibility, which outperforms all alternative approaches on our crystal structure datasets and successfully generalises to MD simulations. We also used ITsFlexible to predict the flexibility of three completely novel CDRH3 loops and experimentally determined their conformations using cryo-EM.
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DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning
Xu, X., Coratella, I., Reys, V., & Bonvin, A. M. J. J. (2026) · Communications Biology · doi:10.1038/s42003-026-10408-4 · Cited by 4 · see citing works
Enhancing antibody-antigen interaction prediction with atomic flexibility
Joubbi, S., Micheli, A., Milazzo, P., Ciano, G., Gagné, S. M., Lió, P., Medini, D., & Maccari, G. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013576 · Cited by 2 · see citing works
Sampling antibody conformational ensembles with ABodyBuilder4-STEROIDSPreprint
Spoendlin, F. C., Cagiada, M., Ifashe, K., Vavourakis, O., & Deane, C. (2026) · bioRxiv · doi:10.64898/2026.04.14.718378 · Cited by 2 · see citing works
Self-citationAnfu, P. K., Madjitey, G., Afreh, J., Abakah, A., Dunyo, P., Manu, P., Gyasi, K. F., Osei‐Poku, P., & Kwarteng, A. (2025) · Medicine Advances · doi:10.1002/med4.70027 · Cited by 1 · see citing works
Moving the antibody: Molecular dynamics for molecular mechanisms and developability
Cagiada, M., & Deane, C. M. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103225 · Cited by 1 · see citing works
Self-citationSequence context decodes multi-state conformational heterogeneity from crystallographic B-factorsPreprint
Wang, Y., Cai, M., Ma, Y., Shi, T., & Wei, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.05.692009 · Cited by 1 · see citing works
Learning conformational flexibility of immune receptors
Shen, L.-C., Yu, D., & Song, J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01133-4
HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody DiscoveryPreprint
Dell'uomo, D., Satz, A., & Averso, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.687005
Scope of nanobodies in hepatopancreatobiliary oncology
Nayek, U., Sharma, T., Kashyap, V. K., Gadad, S. S., Yallapu, M. M., Cobos, E., & Chauhan, S. C. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104666
Stephenson, H., Voicu, D., Novakov, V., Levy, M., & Marsilio, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732304
RetractedAbCVista: a deep learning framework for predicting antibody conformational ensembles
Zong, F., Gan, J., Liu, Y., Qian, W., Li, Y., Li, C., Xiao, Z. J., & Cao, Y. (2026) · Protein & Cell · doi:10.1093/procel/pwag040
Structural quality-tier assessment for TCR-pMHC functional enrichment
Ascunce-París, A., Romero‐Durana, M., Valencia, A., Farriol-Duran, R., & Guallar, V. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1869810
Physicochemical developability insights into human Fab fragments with extended hypervariable loops
Passon, M., Bandera, G., Mayerhöfer, L., Smedt, S. C. D., & Svilenov, H. L. (2026) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2026.104447
Liang, C., Shang, X., Liu, H., Zhou, J., Chen, Y., Zhou, X., Zhu, X., Liu, E., & Wang, A. (2026) · Virology · doi:10.1016/j.virol.2026.111095
- Authors
- Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA
- Scripps Research Institute (US)
- Roche Pharma AG (Germany) (DE)
- Corresponding author
- Spoendlin, Fabian C.
- Deane, Charlotte M.
- ORCID
- Venue
- Nature Machine Intelligence
- Publisher
- openRxiv
- Published
- 2025-03-19
- Volume
- 7
- Issue
- 10
- Pages
- 1755-1767
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC12552124
- OpenAlex
- W4415380213
- Cited by
- 20
- References
- 82
- Citations by year
- 2026: 13 · 2025: 7
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Roche
- Research Councils UK (EP/S024093/1)
- Royal Commission for the Exhibition of 1851
- Engineering and Physical Sciences Research Council (EP/S024093, EP/S024093/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- hypervariable region, CDR3 loop, T cell receptor, graph neural networks, protein flexibility, molecular dynamics simulation, cryo-electron microscopy, protein structure prediction
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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.
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