TCR-Peptide Interaction Prediction Using Docking-Based Pseudo-Labeling
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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 computational efficiency and accuracy in personalized medicine and targeted vaccine development.
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 on existing TCR-peptide datasets, then the model can be trained and deployed, but the prediction accuracy is insufficient due to lack of data diversity
Solution Approach 1:
The patent applies preliminary action by using physical modeling (docking simulations) to pre-generate synthetic TCR-peptide interaction data before training the deep learning model. This pre-generated data is then used to augment the training dataset, allowing the model to learn from a more diverse set of interactions without requiring extensive experimental data collection.
Solution Approach 2:
The patent uses copying by generating synthetic TCR-peptide interaction data through physical modeling and docking simulations. These computationally generated interaction examples serve as copies that augment the limited experimental data, enabling the model to learn from a broader range of TCR-peptide interactions without conducting numerous wet-lab experiments.
2Measurement precision
If more diverse TCRs and peptides are included in the dataset, then prediction accuracy improves, but computational resources and time required for training increase
Solution Approach 1:
The patent generates synthetic training data through computational docking simulations, creating copies of TCR-peptide interaction examples without requiring time-consuming experimental measurements. This allows rapid generation of diverse training data that accelerates model training while maintaining prediction accuracy.
Solution Approach 2:
The patent replaces the mechanical/experimental system of wet-lab TCR-peptide interaction measurements with computational docking simulations. This substitution eliminates the time-consuming nature of experimental data collection while providing sufficient data diversity for accurate predictions.
3Measurement precision
If physical modeling and pseudo-labeling are used to extend the training dataset, then data diversity and model accuracy improve, but the complexity of the training process increases
Solution Approach 1:
The patent introduces physical modeling (docking simulations) as an intermediary step between raw TCR-peptide sequences and the deep learning model training. This intermediary generates pseudo-labels and synthetic data that bridge the gap between limited experimental data and the need for diverse training examples, simplifying the overall training process by providing structured augmented data.
Solution Approach 2:
The patent performs preliminary data augmentation through physical modeling and pseudo-labeling before the main model training phase. This preliminary preparation of extended training data with accurate labels simplifies the subsequent training process by providing a ready-to-use diverse dataset, reducing the overall complexity of the workflow.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly improves the accuracy and efficiency of predicting TCR-peptide interactions, enhancing the potential for personalized medicine and targeted vaccines in immunotherapy by expanding the training dataset and refining the model through pseudo-labeling.
Implementation Method 1
building TCR structures and peptide structures using the MSA and corresponding structures from a Protein Data Bank (PDB) using MODELLER
Implementation Method 2
generating an extended TCR-peptide training dataset based on docking energy scores determined by docking peptides to TCRs using physical modeling
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.