Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations

• URL: http://arxiv.org/abs/2505.01433v1
• Date: Tue, 22 Apr 2025 20:22:34 GMT
• Title: Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations
• Authors: Cong Qi, Hanzhang Fang, Siqi jiang, Tianxing Hu, Wei Zhi,
• Abstract summary: We present LANTERN, a deep learning framework that combines large-scale protein language models with chemical representations of peptides.<n>Our model demonstrates superior performance, particularly in zero-shot and few-shot learning scenarios.<n>These results highlight the potential of LANTERN to advance TCR-pMHC binding prediction and support the development of personalized immunotherapies.
• Score: 0.39945675027960637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We present LANTERN (Large lAnguage model-powered TCR-Enhanced Recognition Network), a deep learning framework that combines large-scale protein language models with chemical representations of peptides. By encoding TCR \b{eta}-chain sequences using ESM-1b and transforming peptide sequences into SMILES strings processed by MolFormer, LANTERN captures rich biological and chemical features critical for TCR-peptide recognition. Through extensive benchmarking against existing models such as ChemBERTa, TITAN, and NetTCR, LANTERN demonstrates superior performance, particularly in zero-shot and few-shot learning scenarios. Our model also benefits from a robust negative sampling strategy and shows significant clustering improvements via embedding analysis. These results highlight the potential of LANTERN to advance TCR-pMHC binding prediction and support the development of personalized immunotherapies.

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