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THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy applications.

Xu Long¹, Qiang Yang¹, Weihe Dong¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Plos Computational Biology

|September 12, 2025

Summary

This summary is machine-generated.

Predicting T-cell receptor (TCR) binding to neoantigens is key for cancer immunotherapy. THLANet, a deep learning model, accurately predicts TCR-neoantigen interactions using sequence data, advancing anti-tumor immunity research.

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Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

Adaptive immunity is crucial for anti-tumor responses, relying on T-cell receptor (TCR) recognition of tumor antigens presented by human leukocyte antigens (HLA).
The limited ability of TCRs to recognize all potential neoantigens poses a challenge for effective cancer immunotherapy.
Accurate prediction of TCR-neoantigen binding is essential for assessing immunogenicity and guiding therapeutic strategies.

Purpose of the Study:

To develop a deep learning model, THLANet, for predicting the binding specificity between TCRs and neoantigens presented by class I HLA molecules.
To enhance sequence feature representation using evolutionary scale modeling-2 (ESM-2) for improved prediction accuracy.
To provide insights into the structural basis of TCR-antigen interactions.

Main Methods:

Developed THLANet, a deep learning model utilizing ESM-2 for sequence feature extraction.
Constructed a TCR-pHLA binding database using scTCR-seq data to train and validate the model.
Evaluated model performance on clinical cancer data across diverse cancer types.
Analyzed complementarity-determining region (CDR3) sequences and performed alanine scanning simulations.

Main Results:

THLANet accurately predicts TCR-neoantigen binding specificity using only TCR CDR3β, antigen, and class I HLA sequence information.
The model demonstrates clinical potential validated on scTCR-seq data and diverse cancer types.
Analysis provided novel insights into the 3D binding interactions between TCRs and antigens.

Conclusions:

THLANet offers a robust and accurate method for predicting TCR-neoantigen pairing, a significant challenge in immunology.
The model's ability to leverage sequence data simplifies prediction and offers new perspectives on TCR-antigen interactions.
This work has implications for advancing neoantigen discovery and developing personalized cancer immunotherapies.