T-Cell Receptor Prediction With Docking-Based Pseudo-Labels
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Solution Overview
Problem
Conventional systems and methods for predicting T-cell receptor (TCR)-peptide interactions are inefficient and inaccurate due to a lack of diverse TCRs and peptides in the datasets, limiting their effectiveness in personalized medicine and targeted vaccines in immunotherapy.
Innovation Solution
A deep learning model is trained using physical modeling and data-augmented pseudo-labeling, where TCR-peptide pairs are classified and labeled based on docking energy scores, and the model is iteratively retrained until convergence, leveraging a conditional variational autoencoder (cVAE) for TCR generation and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning models are trained using existing datasets, then the training process is simple, but the prediction accuracy is low due to lack of diverse TCRs and peptides
Solution Approach 1:
The patent applies preliminary action by performing physical modeling and docking simulations before the main training process. Specifically, docking energy scores are calculated in advance for TCR-peptide pairs, and these pre-computed scores are then used to generate pseudo-labels that augment the training dataset. This preliminary computation of physical properties enables the model to learn from expanded data without requiring complex real-time calculations during training.
Solution Approach 2:
The patent introduces docking energy scores as an intermediary between the physical TCR-peptide interaction and the deep learning model. These energy scores serve as a bridge that translates complex molecular interactions into quantifiable labels that can be used for supervised learning. The intermediary pseudo-labels generated from these scores enable the model to learn interaction patterns without requiring direct experimental labels for all training samples.
2Measurement precision
If the training dataset is expanded with diverse TCRs and peptides, then prediction accuracy improves, but data collection and processing time increase
Solution Approach 1:
The patent uses copying by generating synthetic training data through physical modeling. Instead of collecting and processing additional experimental data, the system creates copies of TCR-peptide interaction data by computationally modeling docking scenarios. These synthetic data copies, labeled with docking energy scores, are then used to augment the training dataset, effectively expanding data diversity without the time cost of experimental data collection.
Solution Approach 2:
The patent replaces the mechanical process of experimental data collection and processing with computational physics-based modeling. Instead of wet-lab experiments to obtain TCR-peptide interaction data, the system uses in-silico docking simulations to generate interaction data. This substitution of mechanical experimental procedures with computational methods significantly reduces data processing time while maintaining data diversity.
3Measurement precision
If physical modeling and pseudo-labeling are used to expand the dataset, then model accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the computational process into distinct modules: (1) TCR and peptide structure modeling, (2) docking simulation to calculate binding energies, (3) pseudo-label generation from energy scores, and (4) deep learning model training. This segmentation allows each component to be optimized independently and enables parallel processing of different TCR-peptide pairs, reducing overall computational complexity while maintaining accuracy.
Data Source
AI summary
Systems and methods for predicting T-Cell receptor (TCR)-peptide interaction, including training a deep learning model for the prediction of TCR-peptide interaction by determining a multiple sequence alignment (MSA) for 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 Data Bank (PDB) using a MODELLER, and 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. TCR-peptide pairs are classified and labeled as positive or negative pairs using pseudo-labels based on the docking energy scores, and the deep learning model is iteratively retrained based on the extended TCR-peptide training dataset and the pseudo-labels until convergence.