重塑肽-MHC-TCR三元结合为序列融合以用于免疫原性预测
Remodeling Peptide-MHC-TCR Triad Binding as Sequence Fusion for Immunogenicity Prediction
AI总结：
本文提出将肽-MHC-TCR三元结合重塑为序列融合的新建模方法，利用表示学习整合三条序列，在现有基准上实现2.8%至13.3%的预测准确率提升，并通过消融研究验证了模型组件的有效性。
摘要：
三方肽-MHC-TCR相互作用的复杂本质是免疫原性预测中一个关键但尚未被充分探索的领域。传统的TCR-抗原结合研究尚未完全解决三元结合中的复杂依赖关系。在本文中，我们针对这些三方相互作用提出了新的建模方法，利用来自MHC、肽和TCR的序列信息。我们的方法遵循天然序列形式，并与生物学过程保持一致，以提高预测准确性。通过引入表示学习技术，我们提出了一种融合机制来有效整合这三条序列。实证实验表明，我们的模型优于传统方法，在现有基准上实现了2.8%至13.3%的预测准确率提升。我们进一步通过广泛的消融研究验证了我们的方法，证明了所提出模型组件的有效性。模型实现、代码和补充材料（包括一份带有彩色超链接的手稿和一份供数字阅读的技术附录）将在发表后开源。
英文摘要：
The complex nature of tripartite peptide-MHC-TCR interactions is a critical yet underexplored area in immunogenicity prediction. Traditional studies on TCR-antigen binding have not fully addressed the complex dependencies in triad binding. In this paper, we propose new modeling approaches for these tripartite interactions, utilizing sequence information from MHCs, peptides, and TCRs. Our methods adhere to native sequence forms and align with biological processes to enhance prediction accuracy. By incorporating representation learning techniques, we introduce a fusion mechanism to integrate the three sequences effectively. Empirical experiments show that our models outperform traditional methods, achieving a 2.8 to 13.3 percent improvement in prediction accuracy across existing benchmarks. We further validate our approach with extensive ablation studies, demonstrating the effectiveness of the proposed model components. The model implementation, code, and supplementary materials, including a manuscript with colored hyperlinks and a technical appendix for digital viewing, will be open-sourced upon publication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
