College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Research Office, City University of Hong Kong (Dongguan), Dongguan 523000, China.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad475.
The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs.
To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset.
The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.
T 细胞受体 (TCR) 和肽-主要组织相容性复合物 (pMHC) 之间的相互作用对适应性免疫系统至关重要。然而，由于实验数据的有限可用性、序列数据异质性和高实验验证成本，识别这些相互作用具有挑战性。
为了解决这个问题，我们开发了一种新的计算框架，称为 MIX-TPI，用于使用氨基酸序列和物理化学特性预测 TCR-pMHC 相互作用。基于卷积神经网络，MIX-TPI 结合了基于序列和基于物理化学的提取器，以细化 TCR-pMHC 相互作用的表示。每种模态都被投影到模态不变和模态特定的表示中，以捕获不同特征之间的一致性和多样性。然后采用自注意力融合层来形成分类模块。实验结果表明，与其他最先进的方法相比，MIX-TPI 是有效的。MIX-TPI 在互斥评估数据集和配对 TCR 数据集上也表现出良好的泛化能力。
MIX-TPI 的源代码和测试数据可在以下网址获得：https://github.com/Wolverinerine/MIX-TPI。
