Proteome Mapping With Real-Time Peptide Elution Targeting
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Solution Overview
Problem
Current proteome mapping techniques are limited by low throughput, sensitivity, and accuracy, particularly in the analysis of complex samples like plasma, which hinders the identification of disease biomarkers and early disease detection.
Innovation Solution
A high-throughput proteome mapping method using liquid chromatography followed by multiple rounds of mass spectrometry, employing intelligent sectioning, peptide ion selection, and windowing based on machine learning predictions to enhance sensitivity and accuracy, allowing real-time peptide retention time prediction and targeted MS3 quantification without fractionation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing DDA approaches with fractionation are used for proteome mapping, then broad proteome coverage is achieved, but throughput is low and analysis time is long
Solution Approach 1:
The patent applies preliminary action by predicting peptide elution times and retention times before the actual MS analysis. Machine learning models predict which peptides will be present and when they will elute, allowing the system to pre-configure the acquisition strategy. This eliminates the need for fractionation while maintaining comprehensive coverage, as the system knows in advance what to look for and when, thereby increasing throughput without sacrificing coverage.
Solution Approach 2:
The patent replaces the mechanical fractionation system with an intelligent prediction and selection system. Instead of physically dividing the sample into multiple fractions through chromatography columns, the system uses machine learning models to predict peptide presence and elution times, then selectively analyzes peptides based on these predictions. This substitution of mechanical separation with intelligent selection dramatically reduces analysis time while maintaining proteome coverage.
2Measurement precision
If existing DDA approaches are used for proteome mapping, then peptide identification is achieved, but sensitivity and accuracy are limited
Solution Approach 1:
The patent implements feedback by using machine learning models that continuously learn from predicted peptide elution patterns and adjust the MS acquisition strategy in real-time. The system predicts which peptides will be present, monitors the actual elution, and refines predictions for subsequent peptides. This feedback loop enhances sensitivity by focusing MS detection on predicted peptides while reducing complexity through intelligent prioritization of which peptides to analyze.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting MS acquisition parameters based on predicted peptide properties. The system changes isolation windows, collision energies, and accumulation times based on machine learning predictions of peptide elution behavior. This adaptive parameter adjustment improves detection sensitivity for low-abundance peptides while managing instrument complexity through automated parameter optimization.
3Quantity of substance
If fractionation is performed to achieve broad proteome coverage, then more peptides are detected, but the number of required MS runs increases
Solution Approach 1:
The patent extracts only the essential information needed for proteome mapping by using machine learning predictions to identify and analyze only the peptides that are likely to be present and informative. Instead of analyzing all peptides through fractionation, the system extracts and focuses MS detection on predicted peptides, achieving comprehensive coverage with a single MS run. This extraction approach increases productivity by eliminating redundant analyses while maintaining peptide detection quantity.
4Measurement precision
If traditional MS acquisition methods are used, then peptide identification is achieved, but quantification accuracy is insufficient
Solution Approach 1:
The patent applies preliminary action by predicting peptide elution times and preparing quantification strategies before the actual MS scanning. Machine learning models predict which peptides will elute and when, allowing the system to pre-configure optimal accumulation times and isolation windows for quantification. This preliminary preparation enables accurate quantification in a single MS pass without requiring additional scanning time, as the system is already optimized for the expected peptide elution pattern.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for proteome mapping. One of the methods includes: identifying one or more target peptide sequences for a sample; estimating an elution order of one or more expected peptides from a chromatography column; and initiating generation of a first set of mass spectrometry spectra for the sample. The method also includes detecting peaks within the first set of mass spectrometry spectra to determine a real-time status with respect to the estimated elution order; selecting one or more peptide ions that are (i) observed in the first set of mass spectrometry spectra and (ii) included among the one or more peptides expected to be present in the sample; and initiating generation of a second set of mass spectrometry spectra for the one or more selected peptide ions.