Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins

Frédéric A. Dreyer; Jan Ludwiczak; Karolis Martinkus; Brennan Abanades; Robert G. Alberstein; Pan Kessel; Pranav Rao; Jae Hyeon Lee; Richard Bonneau; Andrew M. Watkins; Franziska Seeger

arXiv · 2507.09054

Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins

Frédéric A. Dreyer·Jan Ludwiczak·Karolis Martinkus·Brennan Abanades·Robert G. Alberstein·Pan Kessel·Pranav Rao·Jae Hyeon Lee·Richard Bonneau·Andrew M. Watkins·Franziska Seeger

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Abstract

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.

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Frédéric A. Dreyer, Jan Ludwiczak, Karolis Martinkus, Brennan Abanades, Robert G. Alberstein, Pan Kessel, Pranav Rao, Jae Hyeon Lee, Richard Bonneau, Andrew M. Watkins, Franziska Seeger. 2025-07-11. Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins. https://arxiv.org/abs/2507.09054

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