Motif Window Scoring for Framework and CDR Identification
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Solution Overview
Problem
Current methods for identifying framework and complementarity-determining regions in adaptive immune receptors are inaccurate and inefficient due to high variability, leading to inconsistent results and misidentification of mutations, especially with insertions and deletions.
Innovation Solution
A method and system that analyze amino acid sequences using motif windows to identify candidate start positions for framework and complementarity-determining regions, generating scores for each position and validating start positions to accurately define these regions, regardless of mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify framework and complementarity-determining regions, then the process is simpler, but the identification accuracy decreases due to high variability and mutations
Solution Approach 1:
The patent segments the immune receptor sequence into multiple motif windows, each analyzing a specific region for conserved patterns. This segmentation allows the system to handle high variability by focusing on localized conserved motifs rather than attempting to analyze the entire highly variable sequence at once, thereby improving identification accuracy while managing complexity through modular analysis
Solution Approach 2:
The patent performs preliminary identification of candidate start positions for framework and CDR regions before final validation. This preliminary action involves scanning for conserved motif patterns that indicate potential region boundaries, which then guides subsequent more detailed analysis. This step-by-step approach improves accuracy by systematically narrowing down possibilities before making final identifications
2Measurement precision
If motif window analysis is performed for each candidate position, then identification precision improves, but computational time increases
Solution Approach 1:
The patent applies partial action by performing motif window analysis on only the most promising candidate start positions rather than exhaustively analyzing every possible position. The system identifies a limited set of candidate positions based on initial scanning, then applies the computationally intensive motif analysis only to these candidates, achieving high precision while significantly reducing total computational time compared to exhaustive analysis
3Reliability
If validation of start positions is implemented, then region identification reliability improves, but processing complexity increases
Solution Approach 1:
The patent implements feedback through a validation process that checks whether identified start positions are consistent with expected biological patterns and constraints. The system validates candidate positions by verifying they align with known framework and CDR region characteristics, providing feedback that confirms or rejects candidate identifications. This feedback mechanism improves reliability by ensuring results meet biological plausibility criteria while managing complexity through rule-based validation
Data Source
AI summary
A method for identifying framework regions and complementarity-determining regions in an amino acid sequence. The amino acid sequence is received. A plurality of candidate start positions is identified within the amino acid sequence for a start position for a selected region of interest. A score is generated for each candidate start position of the plurality of candidate start positions via analysis of a motif window that begins at each candidate start position. The start position for the selected region of interest is identified based on a candidate start position of the plurality of candidate start positions having a highest score.