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Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.

Original publication

DOI

10.1074/mcp.m110.003384

Type

Journal article

Journal

Molecular & cellular proteomics : mcp

Publication Date

11/2011

Volume

10

Addresses

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

Keywords

Peptides, Saccharomyces cerevisiae Proteins, Caenorhabditis elegans Proteins, Drosophila Proteins, Proteome, Proteomics, Amino Acid Sequence, Neural Networks (Computer), Models, Molecular, Reference Standards, Artificial Intelligence, Computer Simulation, Mass Spectrometry, Support Vector Machine