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<p>Understanding the relationship between T cell receptors (TCRs) and human leukocyte antigens (HLAs) is essential to elucidate the specificity of the immune response, uncover mechanisms of autoimmunity, and advance targeted immunotherapies. We have previously developed a deep learning method, DePTH (Deep Learning Prediction of TCR–HLA associations), to predict the association between a TCR and an HLA based on their amino acid sequences. In this work, we demonstrate that DePTH can make accurate predictions of TCR–HLA associations in two additional datasets. We have also investigated the influence of two confounders: TCR generation probability and the sequence length of CDR3 (Complementarity-Determining Region 3), and conclude that DePTH learns additional information beyond these two factors. Building on these insights, we combined training data from the two new datasets to train two new versions of DePTH: DePTH 2.0 and DePTH 2.1.</p>

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Improved Deep Learning Prediction of TCR–HLA Associations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Fumin Li,
  • Si Liu,
  • Wei Sun

摘要

Understanding the relationship between T cell receptors (TCRs) and human leukocyte antigens (HLAs) is essential to elucidate the specificity of the immune response, uncover mechanisms of autoimmunity, and advance targeted immunotherapies. We have previously developed a deep learning method, DePTH (Deep Learning Prediction of TCR–HLA associations), to predict the association between a TCR and an HLA based on their amino acid sequences. In this work, we demonstrate that DePTH can make accurate predictions of TCR–HLA associations in two additional datasets. We have also investigated the influence of two confounders: TCR generation probability and the sequence length of CDR3 (Complementarity-Determining Region 3), and conclude that DePTH learns additional information beyond these two factors. Building on these insights, we combined training data from the two new datasets to train two new versions of DePTH: DePTH 2.0 and DePTH 2.1.