TEPCAM: Prediction of T‐cell receptor–epitope binding specificity via interpretable deep learning

Abstract

The recognition of T‐cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR–epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR–epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR–EPitope identification based on Cross‐Attention and Multi‐channel convolution), a deep learning model that incorporates self‐attention, cross‐attention mechanism, and multi‐channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state‐of‐the‐art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross‐attention layer and mapping them to the three‐dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM.

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

“…The potential of TCRs as therapeutics for cancer and other diseases has led to substantial research effort dedicated to understanding and predicting their pMHC interactions and specificity e.g. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Despite the importance of this task [20], TCR complementarity remains extremely difficult to predict beyond well-explored case studies, with existing computational models failing to generalise to unseen data [21,22].…”

“…Currently, most TCR:pMHC specificity predictors use sequence based features only [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The incorporation of structural information offers a compelling strategy to encourage models to learn more generalisable physicochemical principles of complementarity [24][25][26].…”

TSpred: a robust prediction framework for TCR-epitope interactions based on an ensemble deep learning approach using paired chain TCR sequence data

“…Many machine learning and deep learning-based methods have been developed to predict the interaction between TCRs and epitopes. Deep learning-based methods use a wide variety of architectures, such as the convolutional neural networks (CNNs) (9)(10)(11)(12), long short-term memory (LSTM) (13), autoencoder (13), and attention mechanism (6,14,15). Some tools, such as TITAN (6), ImRex (12), pMTnet (16), epiTCR (17), and TEINet (18), consider only the information of the TCR beta chain.…”