Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
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
Bioinformatics and Systems Biology
Research subject Biochemistry towards Bioinformatics
IdentifiersURN: urn:nbn:se:su:diva-94800ISBN: 978-91-7447-775-7OAI: oai:DiVA.org:su-94800DiVA: diva2:656055
2013-11-29, Magnelisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Schwikowski, Benno, Doctor
Käll, Lukas, Assistant ProfessorElofsson, Arne, Professor
A the time of the doctoral defence the paper nr. 4 had a status Epubl. ahead of print.2013-11-072013-10-142013-10-23Bibliographically approved
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