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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.

SUBMITTER: Muntel J

PROVIDER: S-EPMC4350037 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).

Muntel Jan J Boswell Sarah A SA Tang Shaojun S Ahmed Saima S Wapinski Ilan I Foley Greg G Steen Hanno H Springer Michael M

Molecular & cellular proteomics : MCP 20141203 2

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 detectab ...[more]

PMID: 25473088

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Project description:RationaleThe low molecular weight (LMW) proteins present in circulating body fluids, such as serum and plasma, hold biological significance as possible biomarkers. A major obstacle in mass spectrometry-based proteomics of serum is the presence of abundant high molecular weight proteins which mask the identification and quantitation of lower molecular weight proteins. Traditional methods involve the use of affinity resins to remove high molecular weight proteins, such as albumin and immunoglobulin G, with concomitant loss of lower molecular weight proteins. Considering the importance of depleting high molecular proteins, this paper compares an affinity resin, a gel-filter, and an acetonitrile (ACN) precipitation method to achieve successful removal of high molecular weight proteins and recovery of lower molecular weight proteins.MethodsSerum enrichment was carried out by multiple methods such as with the commercially available serum protein mini kit, ACN precipitation, and a gel filter method. Mass spectrometric runs were carried out on an AB SCIEX ESI QTOF 5600 mass spectrometer. Mass spectrometry analysis of the enriched serum obtained by ACN precipitation and gel filter method was performed for global proteome profiling. Quantitative mass spectrometry using isobaric tags for relative and absolute quantitation (iTRAQ) for ACN-precipitated enriched serum was also carried out.ResultsThe gel filter method, though allowing for the resolution and identification of LMW proteins, was better suited for global proteome analysis and not preferred for quantitative proteomic experiments. In contrast, enrichment by the ACN precipitation method allowed for the reproducible identification and quantitation of LMW proteins having molecular weight ≥4 kDa.ConclusionsUsing only chilled ACN and centrifugation, most of the highly abundant proteins were successfully removed from the serum, while recovering a significant portion of the LMW proteome. A more rapid protocol, which is compatible with iTRAQ labeling, to achieve improved results has been elucidated, thus allowing for better screening and identification of potential biomarkers.

Dataset Information

Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).

Publications

Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment (PPA).

Similar Datasets

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