Protein Assay Deconvolution Using Non-Specific Antibody Panels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current protein identification techniques, such as those relying on mass spectrometry or specific antibodies, are inefficient and time-consuming, particularly when identifying a large number of proteins in complex mixtures.
Innovation Solution
A method involving a substrate with spatially resolved protein portions, application of non-specific affinity reagents, and deconvolution algorithms to infer protein identities based on binding patterns, allowing for rapid and accurate identification of multiple proteins without prior separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mass spectrometry-based methods are used for protein identification, then measurement precision is improved, but productivity deteriorates (time-consuming)
Solution Approach 1:
The protein mixture is segmented into individual protein portions that are spatially separated and immobilized on a substrate. Each protein portion maintains its unique identity through spatial addressing, allowing parallel processing of multiple proteins simultaneously, thereby increasing throughput while maintaining identification accuracy
Solution Approach 2:
The patent replaces the complex mechanical mass spectrometry system with a simpler affinity-based binding system. Non-specific affinity reagents bind to protein epitopes, and binding patterns are decoded computationally, eliminating the need for time-consuming mass spectrometry analysis while maintaining identification precision
2Measurement precision
If specific antibodies are used for protein identification, then measurement precision is improved, but device complexity worsens (requires highly specific reagents)
Solution Approach 1:
The patent employs non-specific affinity reagents that can bind to multiple different protein epitopes rather than requiring highly specific antibodies for each individual protein. These universal reagents recognize common structural features across diverse proteins, simplifying the reagent library while enabling identification of many different proteins through pattern recognition
Solution Approach 2:
The patent introduces non-specific affinity reagents as intermediaries that mediate between the protein mixture and the detection system. These reagents bind to protein epitopes and create binding patterns that serve as intermediatory signals, which are then decoded by computational algorithms to infer protein identities without requiring direct specific antibody-protein matching
3Device complexity
If non-specific affinity reagents are used, then device complexity is reduced, but measurement precision deteriorates (incomplete and non-specific data)
Solution Approach 1:
The patent implements a feedback loop where binding data from non-specific affinity reagents is fed into computational deconvolution algorithms. These algorithms iteratively analyze binding patterns, compare them against expected patterns, and refine protein identity inferences, transforming incomplete and non-specific binding data into precise protein identifications
Solution Approach 2:
The patent changes the parameter of data interpretation from direct reading to computational deconvolution. Instead of requiring each reagent to provide specific, unambiguous data, the system collects multiple non-specific binding signals and uses mathematical deconvolution to extract precise protein identity information, effectively transforming the nature of the measurement data
4Measurement precision
If proteins are separated prior to identification, then measurement precision is improved, but productivity deteriorates (additional time for separation)
Solution Approach 1:
The patent performs preliminary spatial separation of proteins by immobilizing individual protein portions at unique addresses on a substrate before the identification step. This preliminary spatial organization eliminates the need for subsequent separation steps during identification, as each protein's location already provides unique identification information, thereby saving time while maintaining precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the identification of at least 400 different proteins with 50% accuracy 10% faster than mass spectrometry-based methods and up to 1000 proteins with similar accuracy, utilizing a panel of non-specific affinity reagents and computational deconvolution.
Implementation Method 1
obtaining a substrate with portions of one or more proteins conjugated to the substrate such that each individual protein portion has a unique, resolvable, spatial address
Implementation Method 2
applying a fluid containing a first through nth set of one or more affinity reagents to the substrate... determining that each portion of the one or more proteins having an identified unique spatial address contains the one or more epitopes associated with the one or more observed signals
Data Source
AI summary
Methods and systems for identifying a protein within a sample are provided herein. A panel of antibodies are acquired, none of which are specific for a single protein or family of proteins. Additionally, the binding properties of the antibodies in the panel are determined. Further, the protein is iteratively exposed to a panel of antibodies. Additionally, a set of antibodies which bind the protein are determined. The identity of the protein is determined using one or more deconvolution methods based on the known binding properties of the antibodies to match the set of antibodies to a sequence of a protein.