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Related Experiment Video

Updated: Jan 17, 2026

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

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Published on: April 19, 2017

AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.

Cheng-Chi Chao^1,2, Yulun Chiu^3,4, Lucas Yeung¹

¹Terasaki Institute for Biomedical Innovation, Los Angeles, CA, United States.

Frontiers in Immunology

|September 15, 2025

Summary

AI models like AlphaFold 3 can now predict T cell receptor interactions with peptide-MHC complexes. This breakthrough aids in identifying disease-related epitopes for improved immunotherapies and vaccine development.

Keywords:

AI/ML-driven structure prediction T-cell therapy design TCR-pMHC recognition immunogenicity modeling protein-protein interactions

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Published on: April 29, 2015

Area of Science:

Immunology
Computational Biology
Structural Biology

Background:

T cells have dual roles in immunity and autoimmunity, necessitating precise regulation of T cell receptor (TCR)-peptide/major histocompatibility complex (pMHC) interactions.
Accurate prediction of TCR-pMHC specificity is vital for developing effective treatments for cancer, infections, and autoimmune diseases.
Current predictive models for TCR-pMHC specificity are still in early development stages.

Purpose of the Study:

To evaluate the potential of AlphaFold 3 (AF3) for predicting TCR epitope specificity.
To explore the application of deep learning-based structural modeling in understanding TCR-pMHC interactions.

Main Methods:

Utilized AlphaFold 3 for AI-driven computation of TCR-pMHC interactions.
Assessed the accuracy of AlphaFold 3 in distinguishing valid from invalid epitopes.
Employed in silico high-throughput processes for epitope identification.

Main Results:

AlphaFold 3 demonstrated the capability to model TCR-pMHC interactions.
The model showed increasing accuracy in differentiating immunogenic epitopes.
Successfully identified potential epitopes for vaccine development and therapeutic T cell design.

Conclusions:

Deep learning-based structural modeling, exemplified by AlphaFold 3, shows promise for generalizable TCR-pMHC interaction prediction.
Accurate TCR-pMHC prediction models can significantly advance T-cell-mediated immunotherapy and drug design.
Precise prediction of T-cell immunogenicity holds substantial therapeutic potential for various diseases.