Prev. Article Next Article Table of Contents

Article

Improving Peptide Identification in Proteome Analysis by a Two-Dimensional Retention Time Filtering Approach

Your current credentials do not allow retrieval of the full text.

Purchase the full-text

PDF/HTML,
figures/images,
references and tables,
(where available)

Nico Pfeifer^†^¶, Andreas Leinenbach^§^‡^¶, Christian G. Huber*^§ and Oliver Kohlbacher*^†

Division for Simulation of Biological Systems, Eberhard Karls University Tübingen, 72076 Tübingen, Germany, Department of Chemistry, Instrumental Analysis and Bioanalysis, Saarland University, 66123 Saarbrücken, Germany, and Department of Molecular Biology, Division of Chemistry, University of Salzburg, 5020 Salzburg, Austria

J. Proteome Res., 2009, 8 (8), pp 4109–4115

DOI: 10.1021/pr900064b

Publication Date (Web): June 3, 2009

* To whom correspondence should be addressed. For C.G.H.: e-mail, c.huber@sbg.ac.at; phone, +43(0)662-8044-5704; fax, +43(0)662-8044-5751. For O.K.: e-mail, oliver.kohlbacher@uni-tuebingen.de; phone, +49(0)7071-29-70457; fax, +49(0)7071-29-5152., †

Eberhard Karls University Tübingen.

, ¶

These authors contributed equally to this work.

, ‡

Saarland University.

, §

University of Salzburg.

Section:

Biochemical Methods

Abstract

The combination of a two-dimensional peptide separation scheme based on reversed-phase and ion-pair reversed phase HPLC with a computational method to model and predict retention times in both dimensions is described. The algorithm utilizes statistical learning to establish a retention model from about 200 peptide retention times and their corresponding sequences. The application of retention time prediction to the peptides facilitated an increase in true positive peptide identifications upon lowering mass spectrometric scoring thresholds and concomitantly filtering out false positives on the basis of predicted retention times. An approximately 19% increase in the number of peptide identifications at a q-value of 0.01 was achievable in a whole proteome measurement.

Keywords:

proteome analysis; two-dimensional separation; tandem mass spectrometry; retention time prediction; statistical learning; Sorangium cellulosum

View: Full Text HTML | Hi-Res PDF | PDF w/ Links

Citing Articles

View all 2 citing articles

Citation data is made available by participants in CrossRef's Cited-by Linking service. For a more comprehensive list of citations to this article, users are encouraged to perform a search in SciFinder.

This article has been cited by 2 ACS Journal articles (2 most recent appear below).

Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
Luminita Moruz, Daniela Tomazela, and Lukas Käll
Journal of Proteome Research2010 9 (10), 5209-5216
- Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
  Luminita Moruz, Daniela Tomazela, and Lukas Käll
  Journal of Proteome Research2010 9 (10), 5209-5216
  Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Most notably such predictions are used to weed out incorrect peptide−spectrum matches, and to design targeted ...
  Abstract | Full Text HTML | Hi-Res PDF | PDF w/ Links
MUDE: A New Approach for Optimizing Sensitivity in the Target-Decoy Search Strategy for Large-Scale Peptide/Protein Identification
Fabio R. Cerqueira, Armin Graber, Benno Schwikowski and Christian Baumgartner
Journal of Proteome Research2010 9 (5), 2265-2277
- MUDE: A New Approach for Optimizing Sensitivity in the Target-Decoy Search Strategy for Large-Scale Peptide/Protein Identification
  Fabio R. Cerqueira, Armin Graber, Benno Schwikowski and Christian Baumgartner
  Journal of Proteome Research2010 9 (5), 2265-2277
  The target-decoy search strategy has been successfully applied in shotgun proteomics for validating peptide and protein identifications. If, on one hand, this method has proven to be very efficient for error estimation, on the other hand, little attention ...
  Abstract | Full Text HTML | Hi-Res PDF | PDF w/ Links