title: CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches.
abstract: 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.
authors.affiliation: Michael Barber Centre for Mass Spectrometry, The University of Manchester, Manchester, M1 7DN, UK.
authors.forename: Claire E
authors.initials: CE
authors.lastname: Eyers
authors.forename: Craig
authors.initials: C
authors.lastname: Lawless
authors.forename: David C
authors.initials: DC
authors.lastname: Wedge
authors.forename: King Wai
authors.initials: KW
authors.lastname: Lau
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Gaskell
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Hubbard
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D029742:Caenorhabditis elegans Proteins / Q000737:chemistry; D003198:Computer Simulation; D029721:Drosophila Proteins / Q000737:chemistry; D013058:Mass Spectrometry / Q000592:standards*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry / Q000592:standards*; D020543:Proteome / Q000737:chemistry; D040901:Proteomics; D012015:Reference Standards; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry; D060388:Support Vector Machine
chemical_list: D029742:Caenorhabditis elegans Proteins; D029721:Drosophila Proteins; D010455:Peptides; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins