Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908447529762816 |
|---|---|
| author | Dreyer, Frédéric A. Ludwiczak, Jan Martinkus, Karolis Abanades, Brennan Alberstein, Robert G. Kessel, Pan Rao, Pranav Lee, Jae Hyeon Bonneau, Richard Watkins, Andrew M. Seeger, Franziska |
| author_facet | Dreyer, Frédéric A. Ludwiczak, Jan Martinkus, Karolis Abanades, Brennan Alberstein, Robert G. Kessel, Pan Rao, Pranav Lee, Jae Hyeon Bonneau, Richard Watkins, Andrew M. Seeger, Franziska |
| contents | We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Using a comprehensive private dataset of high-resolution antibody structures, we demonstrate superior out-of-distribution performance compared to existing specialized and general protein structure prediction tools. Ibex combines the accuracy of cutting-edge models with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins Dreyer, Frédéric A. Ludwiczak, Jan Martinkus, Karolis Abanades, Brennan Alberstein, Robert G. Kessel, Pan Rao, Pranav Lee, Jae Hyeon Bonneau, Richard Watkins, Andrew M. Seeger, Franziska Biomolecules Machine Learning We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Using a comprehensive private dataset of high-resolution antibody structures, we demonstrate superior out-of-distribution performance compared to existing specialized and general protein structure prediction tools. Ibex combines the accuracy of cutting-edge models with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development. |
| title | Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2507.09054 |