Prediction of Epitope-Associated TCR by Using Network Topological Similarity Based on Deepwalk

Abstract

Currently, there are many tools available online for T-cell epitope prediction. They usually focus on the binding of peptides to major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells (APCs). However, the binding of peptides and MHC complexes to the T-cell receptor (TCR) is also critical for the immune process. Identifying the binding of human epitopes to TCRs will be useful for developing vaccines. It also has great prospects in medical issues such as cancer and autoimmune diseases. We propose a similarity-based TCR-epitope prediction method using a similarity measure. This paper introduces the Deepwalk method to calculate the topological similarity between TCR-TCRs, constructs a TCR similarity network topology, and predicts the correlation between TCRs and epitopes based on known TCR-epitope associations. We selected data from 22 types of epitopes from the VDJDB database and trained models to implement TCR-epitope prediction. We trained a model on the data from the 22 types of epitopes, predicting which epitope each TCR belongs to. To compare with other methods, we also generated a second method involving training a model for each type of epitope so that we can predict which TCR is bound to the epitope from a large pool of TCRs. We used the ROC curve, PR curve and other evaluation indicators to evaluate our model in 10-fold cross-validation. In the first model, the AUC value of our method is 0.926, and that of the support vector machine (SVM) method is 0.924. Considering that no one has ever used the first prediction model, we used the second method for the predictions. The results show better predictive performance compared to the SVM method, TCRGP method and random forest method. Our AUC values range from 0.660 to 0.950. The experimental results show that our method outperforms other methods in TCR-epitope prediction, which can help predict the TCR-epitope.