BACKGROUND: Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence. RESULTS: We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model. CONCLUSIONS: Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.
BACKGROUND: Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence. RESULTS: We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model. CONCLUSIONS: Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.
Entities:
Keywords:
Computational proteomics; De novo peptide sequencing; Liquid chromatography; Mass spectrometry; Peptide identification
Authors: Hannes L Röst; George Rosenberger; Pedro Navarro; Ludovic Gillet; Saša M Miladinović; Olga T Schubert; Witold Wolski; Ben C Collins; Johan Malmström; Lars Malmström; Ruedi Aebersold Journal: Nat Biotechnol Date: 2014-03 Impact factor: 54.908
Authors: Hannes L Röst; Timo Sachsenberg; Stephan Aiche; Chris Bielow; Hendrik Weisser; Fabian Aicheler; Sandro Andreotti; Hans-Christian Ehrlich; Petra Gutenbrunner; Erhan Kenar; Xiao Liang; Sven Nahnsen; Lars Nilse; Julianus Pfeuffer; George Rosenberger; Marc Rurik; Uwe Schmitt; Johannes Veit; Mathias Walzer; David Wojnar; Witold E Wolski; Oliver Schilling; Jyoti S Choudhary; Lars Malmström; Ruedi Aebersold; Knut Reinert; Oliver Kohlbacher Journal: Nat Methods Date: 2016-08-30 Impact factor: 28.547
Authors: Eric F Strittmatter; Lars J Kangas; Konstantinos Petritis; Heather M Mottaz; Gordon A Anderson; Yufeng Shen; Jon M Jacobs; David G Camp; Richard D Smith Journal: J Proteome Res Date: 2004 Jul-Aug Impact factor: 4.466