Pep-TCRNet: Prediction of Multi-Class Peptides by T-cell Receptor Sequences with Deep Learning

Pep-TCRNet is a novel approach to constructing a prediction model that can evaluate the probability of recognition between a TCR and a peptide amino acid sequence while combining inputs such as TCR sequences, HLA types, and VJ genes.

Pep-TCRNet operates in two key steps:

• Feature Engineering: This step processes different types of variables:

• • TCR and peptide amino acid sequencing data: The model incorporates neural network architectures inspired by language representation models and graph representation model to learn the meaningful embeddings.
  • Categorical data: Specialized encoding techniques are used to ensure optimal feature representation for HLA types and VJ genes.

• Prediction Model: The second step involves training a prediction model to evaluate the likelihood of a TCR recognizing a specific peptide, based on the features generated in the first step.