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

Abanades, B.; Wong, W. K.; Boyles, F.; Georges, G.; Bujotzek, A.; Deane, C. M.

doi:10.1101/2022.11.04.514231

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bioRxiv · 10.1101/2022.11.04.514231

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

Abanades, B.·Wong, W. K.·Boyles, F.·Georges, G.·Bujotzek, A.·Deane, C. M.

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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 2.81[A], a 0.09[A] 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 2.89[A], a 0.55[A] 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://zenodo.org/record/7258553).

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Abanades, B., Wong, W. K., Boyles, F., Georges, G., Bujotzek, A., Deane, C. M.. 2022-11-04. ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.. https://doi.org/10.1101/2022.11.04.514231

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