Protein Characterization Using Iterative Affinity Probe Probabilities

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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 iteratively identifies proteins using affinity reagent probes, normalizing probabilities based on binding measurements and detector error rates, and employing a database of protein sequences to enhance accuracy and efficiency.

Engineering Contradictions & Design Principles

VSEngineering 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

Engineering Contradiction:

Improveprotein identification accuracyVSAvoidreagent specificity requirements

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the protein identification task into multiple independent steps: (1) binding measurement of affinity reagents to unknown proteins, (2) iterative probability calculation for each candidate protein, and (3) confidence level assessment. This segmentation allows the system to achieve high measurement precision through computational analysis rather than relying solely on complex reagents.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces binding measurements as an intermediary between the affinity reagents and the final protein identification. Instead of directly using highly specific reagents for identification, the system uses binding measurements as an intermediate step that feeds into iterative probability calculations, thereby reducing the direct complexity requirements of the reagents themselves.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If peptide-read data from mass spectrometry is used, then productivity is improved, but measurement precision deteriorates due to errors in identification

Engineering Contradiction:

Improveidentification speedVSAvoidprotein identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements iterative probability calculations where the system continuously refines its protein identification based on feedback from binding measurements. The probability that a candidate protein is present is recalculated in each iteration based on the binding data, allowing the system to correct errors and improve precision while maintaining high productivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces the mechanical/physical peptide-read process with a computational probability-based system. Instead of relying on direct mass spectrometry reads that can contain errors, the system uses iterative calculations and probability assessments to identify proteins, thereby improving measurement precision while maintaining speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If iterative probability calculations are performed for each candidate protein, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improveprotein identification accuracyVSAvoididentification time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies partial action by performing iterative probability calculations only for candidate proteins that are relevant to the binding measurements, rather than exhaustively analyzing all possible proteins. This selective approach maintains high measurement precision for the identified proteins while reducing the overall time loss by avoiding unnecessary calculations.

Inventive Principle:

Principle #16Partial or excessive action

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 approach significantly reduces errors and improves the identification and quantification of proteins by providing accurate confidence levels and reducing the number of iterations required, thereby enhancing the reliability of protein detection.

Implementation Method 1

each affinity reagent probe configured to selectively bind to one or more candidate proteins among a plurality of candidate proteins

Methodology Applied

Scientific EffectAffinity binding:

Data Source

PatentUS20260004881A1Methods and systems for characterizing proteins

Publication Date: 2026.01.01 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 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.