THLANet: A deep learning framework for predicting TCR-pHLA binding in immunotherapy applications
Fig 2
Workflow of THLANet for predicting T-cell receptor (TCR)-peptide-human leukocyte antigen (pHLA) interactions.
(a) The pipeline of THLANet for predicting the TCR-pHLA triad group interaction process. (b) Detailed architecture of THLANet: The training data exhibit a long-tail distribution. Protein sequences are processed through the ESM2 module and a convolutional neural network (CNN) to capture long-distance dependencies and features. Concurrently, the sequences are initially encoded using the BLOSUM62 matrix and embedded through a transformer encoder. The two feature matrices are fused via a bilinear attention network. In the prediction module, a multilayer perceptron (MLP)-based model predicts the interaction scores between TCR and pHLA. Created with BioRender.