Protein Identification Using Multi-Parameter Probabilistic Decoding
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Solution Overview
Problem
Current protein identification techniques suffer from errors and inefficiencies in identifying and quantifying proteins, particularly in samples of unknown proteins, due to reliance on highly specific and sensitive affinity reagents or peptide-read data.
Innovation Solution
A method and system using affinity reagent probes, combined with protein length, hydrophobicity, and isoelectric point measurements, along with algorithms for empirical data analysis, to accurately identify and quantify proteins by calculating probabilities and confidence levels based on experimental outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly specific and sensitive affinity reagents (such as antibodies) are used for protein identification, then the sensitivity and specificity of detection is improved, but the complexity of the system and the difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the protein identification process into multiple independent measurement types (binding measurements, protein length, hydrophobicity, isoelectric point) that can be performed separately and then integrated computationally. This allows each measurement to be simplified while maintaining overall accuracy through multi-parameter analysis.
Solution Approach 2:
The patent replaces complex mechanical/biological detection systems with computational algorithms that process multiple empirical measurements. Instead of relying solely on complex affinity reagent binding assays, the system uses computer-based probability calculations to identify proteins, simplifying the measurement process while maintaining precision.
2Speed
If peptide-read data from mass spectrometer is used for protein identification, then the speed of analysis is improved, but the manufacturing precision and accuracy of protein identification decreases
Solution Approach 1:
The patent merges multiple types of empirical measurements (binding measurements, protein length, hydrophobicity, isoelectric point) into a unified computational analysis framework. By combining these diverse data types and processing them through integrated algorithms, the system achieves both speed and accuracy in protein identification.
Solution Approach 2:
The patent changes the approach from relying on a single type of rapid measurement (mass spectrometry peptide reads) to using multiple empirical parameters measured together. This multi-parameter approach allows the system to maintain speed while improving accuracy through computational integration of diverse measurement types.
3Reliability
If multiple empirical measurements are collected and processed through computational algorithms, then the reliability of protein identification is improved, but the loss of time and complexity of the process increases
Solution Approach 1:
The patent performs preliminary measurements of multiple empirical parameters (binding measurements, protein length, hydrophobicity, isoelectric point) in parallel before the computational analysis. By collecting all necessary data upfront through simultaneous experiments rather than sequential steps, the system reduces overall time while maintaining high reliability through multi-parameter verification.
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 utilizing a computer-implemented method that enhances the accuracy and efficiency of protein identification in unknown 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.