T-Cell Receptor Repertoire Selection Prediction with Physical Model Augmented Pseudo-Labeling for Personalized Medicine Decision Making

A system for predicting T-Cell receptor (TCR)-peptide interaction, comprising:

• a processor operatively coupled to a non-transitory computer readable storage medium, the processor being configured for:
• training a deep learning model for the predicting TCR-peptide interaction, the training comprising:
• determining a multiple sequence alignment (MSA) for a plurality of TCR-peptide pair sequences from a dataset of TCR-peptide pair sequences using a sequence analyzer;
• building TCR structures and peptide structures using the MSA and corresponding structures from a protein structure database using a MODELLER;
• generating an extended TCR-peptide training dataset based on docking energy scores determined by docking peptides to TCRs using physical modeling based on the TCR structures and peptide structures built using the MODELLER;
• classifying and labeling TCR-peptide pairs as positive or negative pairs using pseudo-labels based on the docking energy scores;
• and
• iteratively retraining the deep learning model based on the extended TCR-peptide training dataset and the pseudo-labels until convergence.

A non-transitory computer readable storage medium comprising a computer readable program operatively coupled to a processor device for predicting T-Cell receptor (TCR)-peptide interaction, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:

• training a deep learning model for the predicting the TCR-peptide interaction, the training comprising:
• determining a multiple sequence alignment (MSA) for a plurality of TCR-peptide pair sequences from a dataset of TCR-peptide pair sequences using a sequence analyzer;
• building TCR structures and peptide structures using the MSA and corresponding structures from a protein structure database using a MODELLER;
• generating an extended TCR-peptide training dataset based on docking energy scores determined by docking peptides to TCRs using physical modeling based on the TCR structures and peptide structures built using the MODELLER;
• classifying and labeling TCR-peptide pairs as positive or negative pairs using pseudo-labels based on the docking energy scores;
• and
• iteratively retraining the deep learning model based on the extended TCR-peptide training dataset and the pseudo-labels until convergence.