Literature DB >> 24313442

Quantifying the effect of competition for detection between coeluting peptides on detection probabilities in mass-spectrometry-based proteomics.

Paul Schliekelman1, Shangbin Liu.   

Abstract

There are many factors that contribute to the variation in detection probabilities of proteins in LC-MS/MS experiments, and currently little is known about their relative importance. In this study, we analyze the effect of competition for detection between coeluting peptides on peptide detection probability. Using a novel method for estimating peptide detection probabilities, we show that these probabilities can vary by an order of magnitude between peptides that elute from the liquid chromatograph at the same time as many other peptides and those that elute with fewer other peptides. To explore these results, we use a mathematical model to show that competition for detection between peptides is expected to be a major source of missed detections in complex mixtures because there will be many MS/MS scanning intervals that contain more coeluting peptides than can be subjected to MS/MS analysis. Our data and simulation results show that the number of coeluting peptides is a primary determinant of whether a peptide will be detected. In our data, this had a several-fold larger effect on peptide detection probability than did peptide abundance. Furthermore, the distribution of elution times for the most frequently detected peptides was strongly shifted toward values where there were few coeluting peptides, indicating that the number of coeluting peptides is a major determinant of whether a peptide is proteotypic.

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Year:  2013        PMID: 24313442     DOI: 10.1021/pr400034z

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  3 in total

1.  Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics.

Authors:  Ludger J E Goeminne; Kris Gevaert; Lieven Clement
Journal:  Mol Cell Proteomics       Date:  2015-11-13       Impact factor: 5.911

2.  The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments.

Authors:  Jonathon J O'Brien; Harsha P Gunawardena; Joao A Paulo; Xian Chen; Joseph G Ibrahim; Steven P Gygi; Bahjat F Qaqish
Journal:  Ann Appl Stat       Date:  2018-11-13       Impact factor: 2.083

3.  Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro.

Authors:  Daryl Wilding-McBride; Laura F Dagley; Sukhdeep K Spall; Giuseppe Infusini; Andrew I Webb
Journal:  PLoS One       Date:  2022-07-07       Impact factor: 3.752

  3 in total

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