Protein Identification via Multi-Parameter Probability Analysis
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Solution Overview
Problem
Current protein identification techniques face challenges in accuracy and efficiency, particularly when dealing with unknown proteins, due to errors in quantification and identification, often relying on sensitive affinity reagents or peptide-read data that may not effectively determine protein presence, absence, or quantity in complex samples.
Innovation Solution
A computer-implemented method that uses empirical measurements such as binding of affinity reagent probes, protein length, hydrophobicity, and isoelectric point to identify proteins, comparing these measurements against a database of protein sequences to calculate probabilities and infer protein identity, thereby reducing errors and improving quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly specific and sensitive affinity reagents are used for protein identification, then the sensitivity and specificity of detection is improved, but the complexity of the identification system and the difficulty of quantification in complex samples increases
Solution Approach 1:
The patent segments the protein identification process into multiple independent measurement dimensions: binding measurements with affinity reagents, protein length measurement, hydrophobicity measurement, and isoelectric point measurement. Each measurement provides independent information that can be processed separately and then integrated computationally, reducing the complexity of any single measurement system while improving overall identification accuracy.
Solution Approach 2:
The patent creates a universal identification system that can handle multiple protein characteristics simultaneously using the same computational framework. The algorithm accepts various measurement types (binding affinity, physical dimensions, chemical properties) and processes them through a unified probability calculation approach, making the system versatile for different protein types and measurement methods without requiring separate specialized systems for each measurement type.
2Productivity
If peptide-read data from mass spectrometry is used, then protein identification can be achieved, but the accuracy and quantification reliability in complex samples decreases
Solution Approach 1:
The patent implements a feedback mechanism where the computational algorithm continuously refines protein identification by comparing multiple measurement types against each other and against database predictions. The system uses observed binding measurements, protein physical properties, and chemical characteristics to iteratively update probability assessments of protein presence, improving quantification accuracy through repeated verification and correction of identification results.
Solution Approach 2:
The patent changes the fundamental parameters used for protein identification from solely mass spectrometry peptide reads to a multi-parameter approach including binding affinity measurements, protein length, hydrophobicity, and isoelectric point. This parameter transformation allows the system to leverage complementary information from different measurement types, improving reliability while maintaining productivity through integrated computational analysis.
3Measurement precision
If multiple empirical measurements are collected and analyzed computationally, then the accuracy and confidence level of protein identification is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary computational preparations by pre-calculating and storing probability relationships between measured parameters and protein identities in a database structure. The system pre-establishes the computational framework and algorithms before actual analysis, allowing rapid processing of new samples through efficient query and comparison operations rather than performing complex calculations in real-time during analysis.
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
This method significantly enhances the accuracy and efficiency of protein identification in unknown samples by providing a confidence level for candidate proteins, reducing false detection rates, and enabling precise quantification of proteins in complex biological samples.
Implementation Method 1
binding measurements of affinity reagent probes configured to selectively bind to one or more 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 identifying a protein in a sample of unknown proteins, comprising receiving information of a plurality of empirical measurements performed on the unknown proteins; comparing the information of empirical measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein among a plurality of candidate proteins; and for each of one or more of the plurality of candidate proteins, generating a probability that the candidate protein generates the information of empirical measurements, a probability that the plurality of empirical measurements is not observed given that the candidate protein is present in the sample, or a probability that the candidate protein is present in the sample; based on the comparison of the information of empirical measurements against the database.