Off-Target Protein Identification Using Whole-Sequence Alignment
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Solution Overview
Problem
Existing methods for identifying off-target proteins are computationally inefficient and require prior knowledge of pocket sequences, limiting their effectiveness and applicability to proteins with known 3D structures.
Innovation Solution
A method that compares the overall protein sequence of a drug target with a protein sequence database, performing multiple sequence alignment to identify positionally corresponding residues and calculate similarity measures, reducing the search space and eliminating the need for prior knowledge of pocket locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pocket-to-pocket comparisons are performed across the human proteome to identify off-target proteins, then identification accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The method segments the protein comparison task by first identifying and extracting only the functionally relevant pocket residues (binding site residues) from each protein sequence, rather than comparing entire protein sequences. This segmentation reduces the comparison space from ~20,000 full proteins to only those containing similar pocket residue patterns, significantly improving computational efficiency while maintaining identification accuracy.
Solution Approach 2:
The invention extracts the essential functional information (pocket residues) from complete protein sequences and uses only these extracted elements for comparison. By taking out and focusing solely on the binding site residues that determine off-target interactions, the method eliminates computational waste from comparing non-critical protein regions while preserving the ability to accurately identify off-targets.
2Reliability
If pocket sequences are required for comparison to identify off-targets, then identification reliability is improved, but the method becomes inapplicable to proteins with unknown pocket structures
Solution Approach 1:
The method performs preliminary identification of pocket residues using sequence-based patterns and conservation analysis before the actual off-target comparison. By pre-identifying functionally critical residues through multiple sequence alignment and conservation scoring, the system prepares the necessary comparison data without requiring prior 3D structural information, enabling reliable off-target identification for proteins with unknown structures.
Solution Approach 2:
The invention replaces the mechanical requirement for 3D pocket structure detection with a sequence-based computational approach. Instead of relying on physical or computational pocket detection algorithms that require known structures, the method uses amino acid sequence patterns, conservation analysis, and co-evolutionary signals to identify functionally equivalent residues, thereby substituting structural mechanics with sequence-based information processing.
3Loss of information
If complete protein sequence comparison is performed, then comprehensiveness of analysis is improved, but computational resources and time are excessively consumed
Solution Approach 1:
The method applies local quality analysis by focusing computational resources only on the locally critical regions (pocket residues) that determine binding specificity and off-target interactions. Rather than uniformly analyzing all protein sequences, the system identifies and intensively compares only the functionally relevant local regions, reducing computational time while preventing information loss in critical areas through targeted conservation and pattern analysis.
Data Source
AI summary
A computer-implemented method for identifying off-target proteins comprises: receiving an indication of a first protein comprising residues of interest for targeting; receiving data indicative of a first whole protein sequence corresponding to the first protein; comparing the first whole protein sequence against a protein sequence database to identify whole protein sequences of other proteins having a threshold level of sequence resemblance to the first whole protein sequence; performing multiple sequence alignment on the other whole protein sequences with respect to the first whole protein sequence; identifying residues within each of the aligned whole protein sequences which positionally correspond with the residues of interest in the first whole protein sequence; determining a measure of similarity between the first protein and each other protein; and identifying one or more of the other proteins as off-target proteins with respect to the drug target based on the measures of similarity.