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Updated: May 30, 2026

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning

Claire E Eyers¹, Craig Lawless, David C Wedge

¹Michael Barber Centre for Mass Spectrometry, The University of Manchester, Manchester, M1 7DN, UK.

Molecular & Cellular Proteomics : MCP

|August 5, 2011

Summary

This summary is machine-generated.

Selecting optimal Q-peptides is crucial for accurate absolute protein quantification using mass spectrometry. CONSeQuence, a novel machine learning method, improves Q-peptide selection by considering peptide properties and structure, enhancing quantification accuracy across species.

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Published on: May 22, 2018

Area of Science:

Proteomics
Analytical Chemistry
Bioinformatics

Background:

Absolute protein quantification via mass spectrometry relies on stable-isotope labeled peptides as internal standards.
Effective selection of these reference peptides (Q-peptides) is critical for method success.
Existing methods for Q-peptide selection lack comprehensive predictive power.

Purpose of the Study:

To develop and validate a novel computational method for optimal Q-peptide selection.
To identify key physicochemical and structural determinants of peptide detectability.
To assess the cross-species applicability of the developed Q-peptide selection predictor.

Main Methods:

Development of CONSeQuence, a consensus predictor integrating four machine learning algorithms.
Validation using independent yeast proteomic datasets.
Analysis of physicochemical properties including charge, hydrophobicity, and secondary structure.
Correlation of peptide properties with protein tertiary structure and experimental detectability.

Main Results:

CONSeQuence significantly outperforms existing methods in predicting optimal Q-peptides without prior experimental data.
Peptide secondary structure, alongside charge and hydrophobicity, is a key factor in detectability.
A counterintuitive preference for buried peptide status was observed for frequently detected peptides.
A predictor trained on yeast data showed efficacy when applied to proteotypic peptides from other species.

Conclusions:

CONSeQuence offers an improved, data-driven approach for Q-peptide selection in mass spectrometry-based absolute protein quantification.
Understanding peptide physicochemical and structural properties enhances prediction accuracy and experimental design.
The computational approach demonstrates potential for broad applicability in cross-species proteomic studies.