Protein Identification Decoding With Probabilistic Multi-Measurement Fusion
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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 calculating probabilities and likelihoods, to accurately identify proteins in a sample, 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 detection sensitivity is improved, but identification accuracy and quantification reliability deteriorate due to errors in recognizing unknown proteins
Solution Approach 1:
The patent segments the protein identification process into multiple independent measurement dimensions: binding measurements from affinity reagents, protein length, hydrophobicity, and isoelectric point. Each dimension provides partial information that is then integrated through algorithms to achieve accurate identification of unknown proteins, resolving the contradiction between sensitivity and reliability
Solution Approach 2:
The patent introduces computational algorithms as intermediaries between the experimental measurements and the final protein identification. These algorithms process and integrate data from multiple measurement types (binding, length, hydrophobicity, isoelectric point) to calculate probabilities and likelihoods, thereby improving identification accuracy while maintaining detection sensitivity
2Difficulty of detecting and measuring
If peptide-read data from mass spectrometry is used for protein identification, then detection capability is improved, but quantification accuracy deteriorates
Solution Approach 1:
The patent creates a universal protein identification system that can handle multiple types of measurements (binding data, physical properties like length and hydrophobicity, and isoelectric point) through a single integrated algorithmic framework. This multi-functional approach enables both detection and accurate quantification by treating all measurement types uniformly in the analysis process
3Reliability
If multiple empirical measurements and probabilistic calculations are performed for protein identification, then identification accuracy is improved, but computational complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing probability tables and likelihood values for different protein characteristics during system setup. During actual protein identification, these pre-computed values are retrieved and combined through simple algorithmic operations, significantly reducing real-time computational complexity while maintaining high identification accuracy
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.