Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach

Weber, J.; Parajuli, G.; Wang, S.; Ratner, V.; Ma, X.; Shoshan, Y.; Zhang, L.; Morrone, J.; Raboh, M.; Hexter, E.; Parthasarathy, P. B.; Gaughan, C.; Makarov, V.; Chu, L.; Hasgur, S.; Juric, I.; Diaz, M.; Srivastava, R.; Knauf, J.; Hassan, K.; Cornell, W.; Alban, T.; Chan, T.

doi:10.64898/2026.05.15.725176

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bioRxiv · 10.64898/2026.05.15.725176

Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach

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Abstract

T cell receptors (TCRs) are critical for immune surveillance and successful adaptive immune response against foreign antigens. TCRs drive this key arm of the immune system through recognition of peptide epitopes presented on MHC complexes. However, they are limited due to their stochastic nature and generation via genetic recombination. In silico design of functional TCRs that target defined peptide epitopes would be of considerable utility but has up until now been unsuccessful. Here, we develop an artificial intelligence (AI)-powered approach using a hybrid physics-based simulation and generative AI that successfully engineers TCRs against defined epitopes presented by MHC-I. We use this approach to design TCRs against two cancer antigens, a HERC1 neoantigen and an immunogenic neoepitope in mutant EGFR. We engineer multiple TCRs against the HERC1 neoantigen which activate T cells in response to exposure to peptide-MHC I and kill cancer cells more effectively than a patient-derived TCR. In addition, we used generative AI to design functional TCRs that target the EGFR T790M neoantigen, engineering greater specificity against the mutant sequence. We present an AI-based approach to TCR design with broad utility for efforts to engineer TCRs and for the development of new cell therapies. One sentence summaryArtificial intelligence-based approach enables the directed engineering of functional TCRs with enhanced features that target cancer neoantigens.

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Weber, J., Parajuli, G., Wang, S., Ratner, V., Ma, X., Shoshan, Y., Zhang, L., Morrone, J., Raboh, M., Hexter, E., Parthasarathy, P. B., Gaughan, C., Makarov, V., Chu, L., Hasgur, S., Juric, I., Diaz, M., Srivastava, R., Knauf, J., Hassan, K., Cornell, W., Alban, T., Chan, T.. 2026-05-19. Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach. https://doi.org/10.64898/2026.05.15.725176

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