Hybrid Deep Learning Model for TCR Binding Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) pairs are time-consuming, technically challenging, and costly, limiting their clinical viability, and there is a need for machine learning approaches to predict TCR binding specificity to enhance immunotherapy development.
Innovation Solution
The use of transfer learning and hybrid protein sequence and structure information to train models that predict TCR binding specificity, incorporating structural rigidity of MHC molecules and flexibility of TCRs and peptides, and applying these models to human tumor sequencing data to generate insights on immunogenicity, prognosis, and treatment response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental methods (tetramer analysis, TetTCR-seq, T-scan) are used to detect TCR-pMHC pairs, then measurement precision is improved, but productivity deteriorates due to time-consuming processes and high costs
Solution Approach 1:
The patent replaces wet-lab experimental methods with a computational machine learning model that processes TCR and pMHC sequence data to predict binding pairs. This substitution eliminates time-consuming laboratory procedures while maintaining predictive accuracy through algorithms trained on experimental data.
Solution Approach 2:
The patent creates a computational model that replicates the binding specificity detection capability of experimental methods. By training on experimental data and generating predictions that mirror experimental outcomes, the model provides a faster, scalable alternative without requiring physical reagents or complex laboratory procedures.
2Measurement precision
If experimental methods are used to detect TCR-pMHC pairs, then measurement precision is improved, but loss of time increases due to technically challenging procedures
Solution Approach 1:
The patent performs preliminary training of the machine learning model using experimental data before deployment. This pre-computation phase allows the model to capture binding patterns in advance, enabling rapid predictions without repeating time-consuming experimental procedures for each new TCR-pMHC pair.
Solution Approach 2:
The patent replaces time-intensive wet-lab procedures with computational predictions that execute in minutes or seconds. The machine learning model processes sequence data through algorithms, eliminating the need for lengthy experimental protocols including cell culture, staining, and flow cytometry analysis.
3Productivity
If machine learning approaches are used to predict TCR binding specificity, then productivity is improved, but measurement precision may deteriorate compared to experimental methods
Solution Approach 1:
The patent performs extensive preliminary training of the machine learning model using large datasets of experimentally validated TCR-pMHC pairs. This pre-computation phase allows the model to learn accurate binding patterns from high-quality experimental data, ensuring predictions match experimental outcomes while maintaining computational speed.
Solution Approach 2:
The patent incorporates feedback mechanisms where the model's predictions can be validated against experimental results, and the model can be retrained to improve accuracy. This iterative process ensures that computational predictions remain aligned with experimental reality, bridging the gap between speed and precision.
4Productivity
If existing machine learning models are used for TCR-pMHC pairing prediction, then productivity is improved, but device complexity increases due to computational requirements
Solution Approach 1:
The patent divides the complex prediction task into separate processing components: sequence input modules, embedding layers that convert sequences to numerical representations, and prediction layers that generate binding probability outputs. This modular architecture simplifies implementation and allows each component to be optimized independently.
Solution Approach 2:
The patent designs a universal machine learning model that can predict binding for multiple MHC alleles and TCR types using the same underlying architecture. This multi-functional approach reduces overall system complexity by eliminating the need for separate models for different antigen systems, while maintaining high prediction speed across diverse applications.
Data Source
AI summary
The disclosed technology relates to a computer-implemented method for predicting T cell receptor (TCR) binding specificities towards T cell antigen targets (namely, peptide-major histocompatibility complexes, pMHCs), and a set of extensions of this method, include prediction of immune-related adverse events (irAEs) using a machine learning model. The method involves obtaining genomic and proteomic data from patients, determining TCR and pMHC sequences by analyzing these data, and predicting binding interactions between T cell antigens and the TCRs. The extensions include: (a) a transfer learning model for improving the predictive performance of a pre-trained TCR-antigen binding model as a foundation model, to enhance prediction for a specific pMHC, (b) a biomarker metric defined based on the output of the TCR-pMHC binding prediction method, for diagnosis, prognosis and response prediction purposes, (c) a method, based on the output of the TCR-pMHC binding prediction method, to select optimal antigens for tumor vaccines.