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<p>Human leukocyte antigen (HLA) molecules play a pivotal role in antigen presentation. Tumor cells present neoantigens on the cell surface via HLA molecules, thereby activating cytotoxic T cells and eliciting immune responses. This process offers critical opportunities for cancer immunotherapy and tumor vaccine development. However, the identification of tumor neoantigens remains challenging due to limitations in data scale, prediction accuracy, and cross-species compatibility of existing methods. To address these challenges, we developed TransBindpMHCI, a transformer-based pan-specific major histocompatibility complex (MHC) peptide binding prediction model. By employing 1,404,492 mass spectrometry-screened MHC-presented peptides for modeling, the model directly captures the authentic processes of peptide generation and presentation. Its dual-tier transformer encoder architecture significantly enhances feature extraction capabilities for peptide-MHC binding patterns while reducing computational complexity. Furthermore, TransBindpMHCI extends prediction coverage to peptides spanning 8–15 amino acids and achieves cross-species compatibility for both human and murine MHC-I molecules. Comprehensive evaluations demonstrate that TransBindpMHCI outperforms existing methods in accuracy, computational efficiency, and generalizability, enabling the identification of more immunogenic neoantigens. This model holds substantial promise for advancing tumor neoantigen validation and personalized vaccine design.</p>

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TransBindpMHCI: a transformer-based model for pan-specific MHC-I peptide binding prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Hu Xu,
  • Yuanli Ni,
  • Zixuan Chai,
  • Xuan Cui,
  • Xia Lei,
  • Limei Liu,
  • Juanjuan Shan,
  • Cheng Qian

摘要

Human leukocyte antigen (HLA) molecules play a pivotal role in antigen presentation. Tumor cells present neoantigens on the cell surface via HLA molecules, thereby activating cytotoxic T cells and eliciting immune responses. This process offers critical opportunities for cancer immunotherapy and tumor vaccine development. However, the identification of tumor neoantigens remains challenging due to limitations in data scale, prediction accuracy, and cross-species compatibility of existing methods. To address these challenges, we developed TransBindpMHCI, a transformer-based pan-specific major histocompatibility complex (MHC) peptide binding prediction model. By employing 1,404,492 mass spectrometry-screened MHC-presented peptides for modeling, the model directly captures the authentic processes of peptide generation and presentation. Its dual-tier transformer encoder architecture significantly enhances feature extraction capabilities for peptide-MHC binding patterns while reducing computational complexity. Furthermore, TransBindpMHCI extends prediction coverage to peptides spanning 8–15 amino acids and achieves cross-species compatibility for both human and murine MHC-I molecules. Comprehensive evaluations demonstrate that TransBindpMHCI outperforms existing methods in accuracy, computational efficiency, and generalizability, enabling the identification of more immunogenic neoantigens. This model holds substantial promise for advancing tumor neoantigen validation and personalized vaccine design.