ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

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 what antigen they bind. 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 much faster than AlphaFold2. For example, on a benchmark of forty recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.86Å, a 0.11Å 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). By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. This dataset contains structural models generated with ABodyBuilder2 for over 148k of paired sequences from OAS. It also contains the weights for all ImmuneBuilder models.