title: Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).
abstract: The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.
authors.affiliation: From the ‡Departments of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA;
authors.forename: Jan
authors.initials: J
authors.lastname: Muntel
authors.affiliation: §Department of Systems Biology, Harvard Medical School, Boston, MA.
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Boswell
authors.affiliation: From the ‡Departments of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA;
authors.forename: Shaojun
authors.initials: S
authors.lastname: Tang
authors.affiliation: From the ‡Departments of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA;
authors.forename: Saima
authors.initials: S
authors.lastname: Ahmed
authors.affiliation: §Department of Systems Biology, Harvard Medical School, Boston, MA.
authors.forename: Ilan
authors.initials: I
authors.lastname: Wapinski
authors.affiliation: §Department of Systems Biology, Harvard Medical School, Boston, MA.
authors.forename: Greg
authors.initials: G
authors.lastname: Foley
authors.affiliation: From the ‡Departments of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA; Michael_springer@hms.harvard.edu hanno.steen@childrens.harvard.edu.
authors.forename: Hanno
authors.initials: H
authors.lastname: Steen
authors.affiliation: §Department of Systems Biology, Harvard Medical School, Boston, MA Michael_springer@hms.harvard.edu hanno.steen@childrens.harvard.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Springer
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D008969:Molecular Sequence Data; D019151:Peptide Library; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000378:metabolism
chemical_list: D019151:Peptide Library; D010455:Peptides