TCR Assay Design Using Predictive Modeling for HLA Binding
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Solution Overview
Problem
Current techniques have limited insight into the nexus between HLA-I/II clusters, global frequencies, and binding across SARS-COV-2 variation, making it difficult to develop effective vaccines or therapeutic treatments that target vulnerabilities of SARS-COV-2 and engage a robust adaptive immune response in the vast majority of the world population.
Innovation Solution
A system and method for designing a T-cell receptor (TCR) assay using processor-based predictive modeling, including training an Artificial Neural Network (ANN) to determine average binding predictions of overlapping peptides, selecting peptide pools, and sequencing T-cell responses to estimate patient states, thereby identifying a minimum set of T-cell receptors for classifying or estimating patient states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current techniques are used to study HLA-I/II clusters and SARS-COV-2 binding, then limited insight is obtained, but developing effective vaccines and therapeutic treatments becomes even more difficult
Solution Approach 1:
The patent replaces traditional labor-intensive wet lab methods with automated computational systems. Machine learning models predict T-cell responses and identify epitopes in silico, substituting manual experimental procedures with algorithm-driven analysis. This automation reduces information loss while managing the complexity of vaccine development through systematic computational pipelines.
Solution Approach 2:
The patent creates computational replicas of biological systems through in silico models. Virtual T-cell response predictions and epitope identification algorithms serve as digital copies of actual immune responses, allowing researchers to study HLA-I/II cluster interactions without physical experimentation. This copying approach preserves information that would otherwise be lost in traditional methods.
2Measurement precision
If traditional TCR assay design methods are used, then comprehensive immune response analysis is limited, but the ability to track healing progress and predict viral infection advancement is insufficient
Solution Approach 1:
The patent replaces manual TCR assay design and interpretation with automated machine learning systems. The computational model analyzes T-cell receptor sequences and predicts patient states automatically, substituting expert manual analysis with algorithm-driven detection. This increases measurement precision while reducing the difficulty of detecting subtle immune responses.
Solution Approach 2:
The patent implements feedback loops where T-cell response data continuously refines the machine learning model's understanding of patient states. The system learns from measured immune responses and improves its predictive accuracy over time, creating a self-enhancing measurement system that increases precision while managing detection complexity.
Data Source
AI summary
A system and method of designing a T-cell receptor (TCR) assay includes the use of processor-based predictive modeling of an HLA binding classifier, T-cell response, sequencing T-cells, and TCR classifier/regression. Particularly, embodiments include feeding a representation of various peptides into a trained HLA binding classifier model configured to determine average binding predictions of overlapping peptides at each position of the viral or cancer protein. Based upon the average binding predictions, one or more peptide pools can be selected and fed into the T-cell response model, along with representative blood samples associated with a patient/patient population. Further, a sequenced resultant T-cell response can be used to detect T-cell response patterns. These detected patterns can be used to train the TCR classifier/regression model to predict or estimate a patient state. Ultimately, a primer can be designed using a detected minimum set of T-cell receptors for classifying or estimating the patient state.