Protein Assay Deconvolution Using Non-Specific Antibody Panels
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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 determine protein identities based on binding patterns, allowing for rapid 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 achieved, but productivity is reduced due to time-consuming processes
Solution Approach 1:
The patent segments the protein identification process into two distinct stages: (1) a rapid screening stage using non-specific affinity reagents to generate binding patterns, and (2) a decoding stage using computational algorithms to infer protein identities. This segmentation allows the time-consuming mass spectrometry to be replaced for initial identification, thereby improving productivity while maintaining measurement precision through the subsequent verification process.
Solution Approach 2:
The patent replaces the mechanical/physical separation and detection processes of mass spectrometry with a biochemical binding assay system. Instead of using mass-to-charge ratio separation, the system uses affinity reagent binding patterns combined with computational decoding, substituting a biochemical-informational approach for a physical-separation approach, thereby reducing analysis time while maintaining identification accuracy.
2Measurement precision
If specific antibodies are used for protein detection, then measurement precision is improved, but device complexity increases due to requiring highly specific reagents
Solution Approach 1:
The patent inverts the conventional approach by using non-specific affinity reagents instead of specific antibodies. Rather than requiring each reagent to bind to a unique protein target, the system uses reagents with broad binding specificity and distinguishes proteins through the unique patterns of binding across multiple reagents. This inversion simplifies reagent requirements while maintaining detection precision through pattern recognition.
Solution Approach 2:
The patent implements universality by using a panel of affinity reagents that can bind to multiple different protein families and types. Each reagent in the panel is designed to recognize common structural motifs or domains across diverse proteins, allowing a single reagent to serve multiple detection functions. This multi-functionality reduces the need for highly specific reagents for each individual protein target.
3Device complexity
If non-specific affinity reagents are used, then device complexity is reduced, but measurement precision may worsen due to lack of protein-specific binding
Solution Approach 1:
The patent implements feedback through computational decoding algorithms that analyze the binding patterns generated by non-specific affinity reagents. The system uses the observed binding responses as feedback to iteratively infer and refine protein identity predictions. This computational feedback loop compensates for the non-specificity of individual reagents by identifying unique pattern signatures that correspond to specific proteins, thereby maintaining measurement precision despite using simplified reagents.
Solution Approach 2:
The patent transitions from a one-dimensional detection approach (single specific antibody binding to single target) to a multi-dimensional approach where multiple non-specific reagents create a binding pattern vector for each protein. By adding the dimension of pattern recognition across multiple reagents, the system compensates for the loss of individual reagent specificity, maintaining measurement precision through the combined information from the reagent panel.
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 affinity reagents that are not specific to individual proteins or families.
Implementation Method 1
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.