Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.
GENINUS Inc., Seoul 05836, South Korea.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae472.
Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data.
We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction.
Source code is available at https://github.com/ha01994/TSpred.
预测 T 细胞受体 (TCR)-表位相互作用对于生物医学研究中的许多应用非常重要，例如癌症免疫疗法和疫苗设计。由于可用数据稀缺，TCR-表位相互作用的预测仍然具有挑战性，尤其是对于新的表位。
我们提出了 TSpred，这是一种新的基于配对链 TCR 数据的泛特异性 TCR 结合特异性预测的深度学习方法。我们通过结合 CNN 和注意力机制的预测能力开发了一个稳健的模型，该模型可以很好地推广到看不见的表位。特别是，我们设计了一种互惠注意力机制，专注于提取 TCR-表位相互作用的基础模式。通过对我们模型的全面评估，我们发现 TSpred 在可见和不可见表位特异性预测任务中均达到了最先进的性能。此外，与其他预测器相比，TSpred 对数据集中文肽不平衡相关的偏差更稳健。此外，我们模型的互惠注意力组件通过捕获结构上重要的结合区域来实现模型可解释性。结果表明，TSpred 是 TCR-表位结合预测任务的一种稳健可靠的方法。
源代码可在 https://github.com/ha01994/TSpred 上获得。
