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Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). (Lukas Käll)ORCID iD: 0000-0002-5034-5379
Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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2013 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 12, 5730-5741 p.Article in journal (Refereed) Published
Abstract [en]

In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2013. Vol. 12, no 12, 5730-5741 p.
Keyword [en]
mass spectrometry, shotgun proteomics, mass fingerprinting
National Category
Biochemistry and Molecular Biology
URN: urn:nbn:se:su:diva-94614DOI: 10.1021/pr400705qISI: 000328231300033OAI: diva2:654554

AuthorCount: 6

Funding Agencies:

Swedish Research Council;   U.S. National Science Foundation (MRI)  0923536; American Recovery and Reinvestment Act (ARRA) funds  R01 HG005805;  National Institute of General Medical Sciences/Center for Systems Biology  2P50 GM076547;  Luxembourg Centre for Systems Biomedicine;   University of Luxembourg  

Available from: 2013-10-07 Created: 2013-10-07 Last updated: 2014-01-13Bibliographically 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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