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Tumor cell expression of antigens forms the foundation for inducing this adaptive response. However, the human leukocyte antigens (HLA)-restricted recognition of antigens by T-cell receptors (TCR) limits their ability to detect all neoantigens, with only a small subset capable of activating T-cells. Accurately predicting neoantigen binding to TCR is, therefore, crucial for assessing their immunogenic potential in clinical settings. We present THLANet, a deep learning model designed to predict the binding specificity of TCR to neoantigens presented by class I HLAs. THLANet employs evolutionary scale modeling-2 (ESM-2), replacing the traditional embedding methods to enhance sequence feature representation. Using scTCR-seq data, we obtained the TCR immune repertoire and constructed a TCR-pHLA binding database to validate THLANet\u2019s clinical potential. The model\u2019s performance was further evaluated using clinical cancer data across various cancer types. Additionally, by analyzing divided complementarity-determining region (CDR3) sequences and simulating alanine scanning of antigen sequences, we provided new insights into the 3D binding interactions of TCRs and antigens. Predicting TCR-neoantigen pairing remains a significant challenge in immunology, THLANet provides accurate predictions using only the TCR sequence (CDR3\n                    <jats:italic>\u03b2<\/jats:italic>\n                    ), antigen sequence, and class I HLA, offering novel insights into TCR-antigen interactions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013050","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:02:27Z","timestamp":1757700147000},"page":"e1013050","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy 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