Machine Learning Protein Concentration Estimation

Solution Overview

Problem

Current methods for determining protein compositions in biological samples are costly and labor-intensive, requiring expensive laboratory techniques such as X-ray crystallography or spectrometry, which can be inefficient and resource-heavy.

Innovation Solution

A computer-implemented method using machine learning analysis to predict protein concentrations in heterogeneous samples by generating a synthetic dataset based on protein signature or fingerprint data, training a model without protein-specific calibration, and estimating the percentage of a specific protein of interest (POI) using amino acid analysis (AAA) data, potentially reducing the need for expensive laboratory techniques.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional laboratory techniques such as X-ray crystallography or spectrometry are used to determine protein compositions, then measurement precision is improved, but loss of time and use of energy increase significantly

Engineering Contradiction:

Improveprotein composition accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent creates synthetic AAA datasets that replicate the characteristics of real experimental data without requiring actual laboratory measurements. These synthetic copies contain the same statistical properties and patterns as real protein analysis data, allowing the machine learning model to learn from them and make accurate predictions on real samples, thus avoiding time-consuming physical experiments

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces traditional mechanical and chemical laboratory techniques (X-ray crystallography, spectrometry, HPLC) with a computational machine learning system. The system uses amino acid composition data processed through trained algorithms to predict protein concentrations, substituting physical measurement processes with information processing and pattern recognition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If traditional laboratory techniques are used to determine protein compositions, then measurement precision is improved, but cost and device complexity increase

Engineering Contradiction:

Improveprotein composition accuracyVSAvoidlaboratory equipment requirements

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent creates synthetic AAA datasets that replicate the characteristics of real experimental data without requiring actual laboratory measurements. These synthetic copies contain the same statistical properties and patterns as real protein analysis data, allowing the machine learning model to learn from them and make accurate predictions on real samples, thus avoiding time-consuming physical experiments

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces traditional mechanical and chemical laboratory techniques (X-ray crystallography, spectrometry, HPLC) with a computational machine learning system. The system uses amino acid composition data processed through trained algorithms to predict protein concentrations, substituting physical measurement processes with information processing and pattern recognition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If protein-specific calibration is performed using traditional methods, then measurement precision is improved, but loss of time and productivity decrease

Engineering Contradiction:

Improveprotein concentration accuracyVSAvoidcalibration speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent trains a single machine learning model on synthetic datasets that encompass multiple protein types and scenarios. This universal model can predict concentrations for different proteins of interest without requiring separate calibration procedures for each protein, making the system multi-functional and highly productive

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent performs comprehensive model training in advance using extensive synthetic datasets that cover various protein compositions and conditions. This preliminary training establishes a ready-to-use prediction system that can immediately analyze real samples without requiring time-consuming calibration steps for each new analysis

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240161869A1Systems and methods for algorithmically estimating protein concentrations

Publication Date: 2024.05.16 CLARA FOODS

AI summary

Disclosed is a computer-implemented method and system for estimating protein concentrations. The method comprises first generating a synthetic dataset based at least on protein signature or fingerprint data. Then, the method comprises training a model using in part the synthetic dataset, without requiring protein-specific calibration or training. Finally, the method comprises using the model to estimate or predict a percentage amount of a specific protein of interest (POI) in one or more heterogeneous samples, even if the POI was not used in modeling at the time of training.