Protein Identification Using Multi-Property Probabilistic Decoding

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current protein identification techniques are prone to errors and inefficiencies, particularly when identifying unknown proteins in a sample, due to reliance on highly specific and sensitive affinity reagents or peptide-read data, which can lead to inaccurate quantification and identification.

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 calculate probabilities and likelihoods of protein identity, thereby improving identification and quantification of proteins in a sample.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If highly specific and sensitive affinity reagents are used for protein identification, then identification specificity is improved, but measurement precision and reliability deteriorate due to errors in identifying unknown proteins

Engineering Contradiction:

Improveprotein identification accuracyVSAvoidquantification reliability

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The protein identification process is segmented into multiple independent measurement dimensions: binding measurements from affinity reagent probes, protein length, hydrophobicity, and isoelectric point. Each dimension provides independent evidence that is combined through algorithms to arrive at a reliable identification, reducing reliance on any single measurement type and improving overall accuracy and reliability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Computational algorithms serve as intermediaries that integrate multiple measurement types (binding measurements, protein length, hydrophobicity, isoelectric point) to calculate probabilities of protein identity. These algorithms mediate between raw measurement data and final identification conclusions, enabling more reliable quantification by weighing different evidence sources appropriately.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If peptide-read data from mass spectrometry is used, then identification speed is improved, but measurement precision deteriorates due to limited peptide sequence information

Engineering Contradiction:

Improveidentification speedVSAvoidprotein identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The method uses affinity reagent probes that can detect multiple protein characteristics simultaneously through binding measurements, while also incorporating protein length, hydrophobicity, and isoelectric point data. This multi-functional approach enables accurate identification without requiring extensive peptide sequencing, maintaining speed while improving precision through complementary measurement types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If multiple empirical measurements are combined for protein identification, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improveprotein identification accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Multiple measurement types (binding measurements from affinity reagent probes, protein length, hydrophobicity, isoelectric point) are merged into a unified identification system. The algorithms integrate these diverse data sources by calculating probabilities of protein identity, creating a cohesive system that improves precision through synthesis of complementary information rather than treating measurements as separate complex subsystems.

Inventive Principle:

Principle #5Merging (Combining)

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

The method significantly reduces errors in protein identification and enhances quantification accuracy by utilizing a combination of empirical measurements and computational analysis to determine protein presence and identity.

Implementation Method 1

binding measurements of affinity reagent probes configured to selectively bind to one or more candidate proteins

Methodology Applied

Scientific EffectAffinity binding:

Implementation Method 2

protein length, protein hydrophobicity, and isoelectric point

Methodology Applied

Scientific EffectHydrophobicity measurement:

Implementation Method 3

protein length, protein hydrophobicity, and isoelectric point

Methodology Applied

Scientific EffectIsoelectric point measurement:

Data Source

PatentUS20250391496A1Decoding approaches for protein identification

Publication Date: 2025.12.25 NAUTILUS SUBSIDIARY INC

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.