pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Han, Y.; Yang, Y.; Tian, Y.; Fattah, F. J.; Itzstein, M. S. v.; Zhang, M.; Kang, X.; Yang, D. M.; Liu, J.; Xue, Y.; Liang, C.; Raman, I.; Zhu, C.; Xiao, O.; Hu, Y.; Dowell, J. E.; Homsi, J.; Rashdan, S.; Yang, S.; Gwin, M. E.; Hsiehchen, D.; Gloria-McCutchen, Y.; Pan, K.; Wu, F.; Gibbons, D.; Wang, X.; Yee, C.; Huang, J.; Reuben, A.; Cheng, C.; Zhang, J.; Gerber, D. E.; Wang, T.

doi:10.1101/2023.12.01.569599

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bioRxiv · 10.1101/2023.12.01.569599

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

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Abstract

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

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Han, Y., Yang, Y., Tian, Y., Fattah, F. J., Itzstein, M. S. v., Zhang, M., Kang, X., Yang, D. M., Liu, J., Xue, Y., Liang, C., Raman, I., Zhu, C., Xiao, O., Hu, Y., Dowell, J. E., Homsi, J., Rashdan, S., Yang, S., Gwin, M. E., Hsiehchen, D., Gloria-McCutchen, Y., Pan, K., Wu, F., Gibbons, D., Wang, X., Yee, C., Huang, J., Reuben, A., Cheng, C., Zhang, J., Gerber, D. E., Wang, T.. 2023-12-04. pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding. https://doi.org/10.1101/2023.12.01.569599

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