[Prediction of MHC II antigen peptide-T cell receptors binding based on foundation model].

Q4 Medicine
Minrui Xu, Siwen Zhang, Manman Lu, Yuan Gao, Menghuan Zhang, Yong Lin, Lu Xie
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引用次数: 0

Abstract

The specific binding of T cell receptors (TCRs) to antigenic peptides plays a key role in the regulation and mediation of the immune process and provides an essential basis for the development of tumour vaccines. In recent years, studies have mainly focused on TCR prediction of major histocompatibility complex (MHC) class I antigens, but TCR prediction of MHC class II antigens has not been sufficiently investigated and there is still much room for improvement. In this study, the combination of MHC class II antigen peptide and TCR prediction was investigated using the ProtT5 grand model to explore its feature extraction capability. In addition, the model was fine-tuned to retain the underlying features of the model, and a feed-forward neural network structure was constructed for fusion to achieve the prediction model. The experimental results showed that the method proposed in this study performed better than the traditional methods, with a prediction accuracy of 0.96 and an AUC of 0.93, which verifies the effectiveness of the model proposed in this paper.

[基于基础模型预测MHC II抗原肽- t细胞受体结合]。
T细胞受体(TCRs)与抗原肽的特异性结合在免疫过程的调节和调解中起着关键作用,为肿瘤疫苗的开发提供了重要的基础。近年来的研究主要集中在主要组织相容性复合体(MHC) I类抗原的TCR预测上,而MHC II类抗原的TCR预测研究还不够充分,还有很大的改进空间。本研究利用ProtT5大模型研究MHC II类抗原肽与TCR预测的结合,探索其特征提取能力。此外,对模型进行微调以保留模型的底层特征,并构建前馈神经网络结构进行融合以实现预测模型。实验结果表明,本文方法的预测精度为0.96,AUC为0.93,优于传统方法,验证了本文模型的有效性。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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