PERSPECTIVE article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1651533
This article is part of the Research TopicAdvances in Therapeutic Cancer Vaccines-Mechanisms and developmentView all articles
AI/ML-Empowered Approaches for Predicting T Cell-Mediated Immunity and Beyond
Provisionally accepted- 1Terasaki Institute for Biomedical Innovation, Los Angeles, United States
- 2The University of Texas MD Anderson Cancer Center, Houston, United States
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T cells play a dual role in various physiopathological states, capable of eliminating tumors and infected cells, while also playing a pathogenic role when activated by autoantigens, causing self-tissue damage. The regulation of T cell-peptide/major histocompatibility complex (TCR-pMHC) recognition is crucial for maintaining disease balance and treating cancer, infections, and autoimmune diseases. Despite efforts, predictive models of TCR-pMHC specificity are still in the early stages. Inspired by advances in protein structure prediction via deep neural networks, we evaluated AlphaFold 3 (AF3)-based AI computation as a method to predict TCR epitope specificity. We demonstrate that AlphaFold can model TCR-pMHC interactions, distinguishing valid epitopes from invalid ones with increasing accuracy. Immunogenic epitopes can be identified for vaccine development through in silico high-throughput processes. Additionally, higher-affinity and specific T cells can be designed to enhance therapy efficacy and safety. An accurate TCR-pMHC prediction model is expected to greatly benefit T-cell-mediated immunotherapy and aid drug design. Overall, precise prediction of T-cell immunogenicity holds significant therapeutic potential, allowing the identification of peptide epitopes linked to tumors, infections, and autoimmune diseases. Although there is much work to be done before these predictions achieve widespread practical use, we are optimistic that deep learningbased structural modeling is a promising pathway for the generalizable prediction of TCR-pMHC interactions.
Keywords: TCR-pMHC recognition, AI/ML-driven structure prediction, Immunogenicity modeling, T-cell therapy design, protein-protein interactions
Received: 21 Jun 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Chao, Chiu, Yee, Jiang and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chongming Jiang, Terasaki Institute for Biomedical Innovation, Los Angeles, United States
Xiling Shen, The University of Texas MD Anderson Cancer Center, Houston, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.