Iterative Protein Identification Using Affinity Probe Binding Data
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Solution Overview
Problem
Current protein identification techniques suffer from errors and inefficiencies in identifying and quantifying unknown proteins, particularly due to reliance on highly specific and sensitive affinity reagents or peptide-read data from mass spectrometry.
Innovation Solution
A computer-implemented method using iterative calculations with affinity reagent probes to selectively bind to candidate proteins, incorporating detector error rates and normalization techniques to enhance accuracy and efficiency in protein identification and quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly specific and sensitive affinity reagents are used for protein identification, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the protein identification task into multiple independent affinity reagent probes, each targeting specific protein families or domains. Instead of relying on a single complex reagent system, the method uses a panel of simpler, specialized probes that can be independently selected and combined, reducing overall system complexity while maintaining high identification accuracy through computational integration of probe results.
Solution Approach 2:
The patent creates a universal protein identification system where a single set of affinity reagent probes can be applied across multiple protein families and sample types. The probes are designed with broad applicability, and the computational framework provides universal analysis capabilities, eliminating the need for custom reagent development for each specific protein target and reducing long-term system complexity.
2Measurement precision
If peptide-read data from mass spectrometry is used, then measurement precision is improved, but loss of time and equipment complexity increase
Solution Approach 1:
The patent replaces the complex mechanical mass spectrometry system with a simpler affinity reagent-based detection system combined with computational analysis. Instead of using expensive, time-consuming mass spectrometry hardware, the method uses readily available affinity reagents and digital signal processing to achieve comparable or superior protein identification and quantification, significantly reducing analysis time and equipment requirements.
3Measurement precision
If iterative calculations with multiple affinity reagent probes are used, then measurement precision is improved, but computing time and processing complexity increase
Solution Approach 1:
The patent performs preliminary computational preparation by pre-calculating and storing affinity reagent probe characteristics, protein sequence databases, and binding probability matrices before actual analysis. This pre-processing allows the iterative identification process to run much faster during execution, as the system only needs to query pre-computed data rather than perform all calculations in real-time, significantly reducing operational processing time.
Solution Approach 2:
The patent applies partial iterative calculations by stopping the iterative process when sufficient confidence is achieved, rather than continuing until absolute certainty. The system uses a confidence threshold mechanism that allows early termination of analysis when adequate protein identification confidence is reached, reducing unnecessary computational iterations and processing time while maintaining acceptable measurement 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
Significantly reduces errors in protein identification and improves quantification by iteratively generating confidence levels based on binding measurements, achieving high confidence probabilities for candidate proteins.
Implementation Method 1
each affinity reagent probe configured to selectively bind to one or more candidate proteins among a plurality of candidate proteins
Data Source
AI summary
Methods and systems are provided for accurate and efficient identification and quantification of proteins. In an aspect, disclosed herein is a method for iteratively identifying candidate proteins within a sample of unknown proteins, the method comprising receiving information of binding measurements of each of a plurality of affinity reagent probes to the unknown proteins, each affinity reagent probe configured to selectively bind to one or more candidate proteins; comparing at least a portion of the information of binding measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein; and iteratively generating a probability that each of one or more candidate proteins is present in the sample based on the comparison of the information of binding measurements of the candidate proteins against the database comprising the plurality of protein sequences.