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Chromatographic retention time prediction for posttranslationally modified peptides
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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2012 (English)In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 12, no 8, 1151-1159 p.Article in journal (Refereed) Published
Abstract [en]

Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at or can be run via a web-interface at .

Place, publisher, year, edition, pages
2012. Vol. 12, no 8, 1151-1159 p.
Keyword [en]
Bioinformatics, Machine learning, Posttranslational modification, Retention time prediction, Reversed-phase liquid chromatography
National Category
Medical Biotechnology
URN: urn:nbn:se:su:diva-80742DOI: 10.1002/pmic.201100386ISI: 000303918200009OAI: diva2:558047


Available from: 2012-10-01 Created: 2012-09-27 Last updated: 2013-10-16Bibliographically approved
In thesis
1. Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
Open this publication in new window or tab >>Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.

In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.

The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.

The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.

Place, publisher, year, edition, pages
Department of Biochemistry and Biophysics. Stockholm University, 2013. 58 p.
mass spectrometry; reversed-phase liquid chromatography; retention time prediction; bioinfomatics; computational mass spectrometry
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
urn:nbn:se:su:diva-94800 (URN)978-91-7447-775-7 (ISBN)
Public defence
2013-11-29, Magnelisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)

A the time of the doctoral defence the paper nr. 4 had a status Epubl. ahead of  print.

Available from: 2013-11-07 Created: 2013-10-14 Last updated: 2013-10-23Bibliographically approved

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Moruz, Luminita
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