TCR-Peptide Interaction Prediction Using Physical-Model Pseudo-Labeling
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Solution Overview
Problem
Conventional systems for predicting T-cell receptor (TCR)-peptide interactions are inefficient and inaccurate due to a lack of diverse TCRs and peptides in the training datasets, limiting their computational efficiency and accuracy in personalized medicine and targeted vaccine development.
Innovation Solution
A method involving physical modeling and data-augmented pseudo-labeling is employed to extend the training dataset by using docking energies between TCR-peptide pairs, iteratively retraining a deep learning model with pseudo-labels until convergence, incorporating multiple sequence alignment and Protein Data Bank structures to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning models are trained on existing TCR-peptide datasets, then the models can be implemented with current data resources, but the prediction accuracy is limited due to lack of diverse TCRs and peptides in the datasets
Solution Approach 1:
The patent uses pseudo-labeling to create synthetic training examples by copying and adapting existing TCR-peptide interaction data. The system generates artificial training pairs with assigned labels based on similarity to known interactions, effectively multiplying the available training data without requiring additional experimental measurements. This allows the model to learn from expanded dataset while maintaining accuracy on diverse TCR-peptide combinations.
Solution Approach 2:
The patent performs preliminary data augmentation by generating synthetic TCR-peptide pairs and assigning pseudo-labels before actual model training. This pre-processing step creates an extended training dataset that incorporates diverse sequences through in silico modeling and homology-based generation, preparing enhanced training material in advance to improve subsequent prediction accuracy on unseen diverse sequences.
2Quantity of substance
If physical modeling and pseudo-labeling are used to extend the training dataset, then the diversity of TCRs and peptides is improved, but the computational complexity and time for model training increases
Solution Approach 1:
The patent applies partial action by selectively generating pseudo-labeled training examples only for specific underrepresented TCR-peptide combinations rather than exhaustively modeling all possible pairs. The system focuses computational resources on generating synthetic data for diverse but biologically relevant sequences, avoiding unnecessary computation on redundant or low-priority combinations, thus balancing dataset diversity with training efficiency.
Solution Approach 2:
The patent implements skipping by using approximate physical modeling methods that rapidly generate docking energy scores and structural predictions without performing exhaustive molecular dynamics simulations. The system uses simplified energy functions and streamlined homology modeling to quickly produce pseudo-labeled examples, rushing through the data generation process to minimize computational time while still achieving sufficient diversity in the training dataset.
3Measurement precision
If iterative retraining with pseudo-labels is performed until convergence, then the prediction accuracy is enhanced, but the computational resources and training iterations required increase
Solution Approach 1:
The patent implements feedback through iterative retraining where the model's prediction performance is continuously evaluated and used to guide subsequent training iterations. Pseudo-labeled examples are regenerated and re incorporated based on the model's current performance metrics, creating a feedback loop that progressively refines prediction accuracy. The system monitors convergence criteria and adjusts training strategies based on performance feedback to optimize the balance between accuracy improvement and computational complexity.
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.