TCR-pMHC Interaction Analysis With Noise-Adjusted Dextramer Clustering
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Solution Overview
Problem
Existing computational methods for predicting TCR-pMHC specific recognition are limited by low signal-to-noise ratios in high-throughput dextramer binding data and fail to accurately learn complex sequence patterns from full-length TCR sequences, leading to sub-optimal prediction accuracy.
Innovation Solution
A computational framework for processing and normalizing TCR-pMHC binding data using droplet-based single-cell RNA sequencing, including pre-processing, noise adjustment, dextramer and TCR clustering, and generating relative interaction strength data to identify reliable binding events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If highly multiplexed dextramer binding data are used to increase the volume of TCR-pMHC binding data, then the quantity of binding data is improved, but the signal-to-noise ratio deteriorates
Solution Approach 1:
The patent segments the analysis process into multiple stages: initial data processing, intermediate filtering steps, and final validation. By dividing the large dataset into manageable processing stages with intermediate quality checks, the system maintains measurement precision while handling high-volume data.
Solution Approach 2:
The patent applies preliminary filtering and quality control measures before full analysis. Empty droplets are identified and removed early in the process, and preliminary thresholds are established to pre-filter low-quality binding events before they contaminate the final dataset.
2Adaptability or versatility
If deep learning frameworks are used to learn complex patterns from full-length TCR sequences, then the complexity of pattern recognition is improved, but the computational resources required increase
Solution Approach 1:
The deep learning model processes TCR sequences by segmenting them into functionally relevant regions (CDR loops, framework regions, V-J segments). This segmentation allows the model to focus computational resources on the most informative regions while maintaining comprehensive pattern recognition capability.
Solution Approach 2:
The patent applies different processing strategies to different regions of the TCR sequence. CDR loops receive more intensive analysis due to their critical role in antigen binding, while framework regions are processed with optimized algorithms that reduce computational overhead while preserving essential information.
3Device complexity
If traditional single multimer sorting or antigen re-exposure assays are used, then the experimental complexity is reduced, but the diversity and quantity of training data deteriorate
Solution Approach 1:
The patent employs a unified high-throughput screening platform that can simultaneously test multiple TCR-pMHC combinations in a single experiment. This multi-functional approach replaces multiple separate traditional assays, increasing data diversity while maintaining manageable experimental complexity through standardization.
Solution Approach 2:
The system uses feature-barcoded dextramers that allow digital copying and tracking of binding events. Each dextramer carries a barcode that enables precise counting and tracking of binding interactions, effectively amplifying the information content from each experimental run without requiring proportional increases in experimental complexity.
Data Source
AI summary
A computational framework for high-throughput mapping, validating, and predicting receptor sequence interactions is described. A method includes pre-processing sequence data, adjusting data for noise, generating intermediate strength of interaction data, aggregating the intermediate strength of interaction data based on dextramer clustering and based on TCR clustering, and generating final relative strength of interaction data that identifies reliable TCR-pMHC binding events.