(vi) Bioinformatics and Proteomics

We are establishing bioinformatics tools that are aimed to facilitate mass spectrometric analyses of prokaryotic as well as for eukaryotic organisms with complex gene structures. Here we are aiming to use mass spectrometric data for genomic data mining and annotation of protein-coding genes. We devised an algorithm, the GenomicPeptideFinder (GPF), which takes advantage of de novo amino acid predictions of MS/MS data enabling detection of intron-split and/or alternatively spliced peptides when deduced from genomic DNA (Allmer et al., 2004, Allmer et al., 2006, Specht et al.,2011a). Further, we generated a platform for proteomic analysis (Specht et al. 2011b), and we devised different algorithms aimed for improved mass spectrometric data mining, including quantitative analyses using qTRACE (Terashima et al., 2010,) and pyQms (Leufken et al., 2017), we developed a fast parser for mzML data (Bald et al., 2011, Kosters et al., 2018) and established a python-based tool named “SugarPy” for de novo identification of N-glycosylated peptides and their N-glycan compositions from bacteria to human (Esquivel et al., 2016, Schulze et al., 2017, 2018, Oltmanns 2020, Schulze et al., 2020). With Crosslinx, we also devised a tool for the analysis of crosslinked peptides via mass spectrometry (Ozawa et al., 2018).

For proteomic analyses the laboratory is equipped with a hybrid linear ion-trap mass spectrometer (Q-Exactive plus-Orbitrap, Thermo Fischer) that is coupled to an UltiMate Nano liquid chromatography system.

Proteomics Podcast (10.11.06)

Schulze S, Oltmanns A, Fufezan C, Krägenbring J, Mormann M, Pohlschroder M, Hippler M (2020)
SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides.
Bioinformatics in press

Oltmanns A, Hoepfner L, Scholz M, Zinzius K, Schulze S, Hippler M (2020)
Novel Insights Into N-Glycan Fucosylation and Core Xylosylation in C. reinhardtii.
Front Plant Sci 10: 1686

Schulze S, Oltmanns A, Machnik N, Liu G, Xu N, Jarmatz N, Scholz M, Sugimoto K, Fufezan C, Huang K, Hippler M (2018)
N-Glycoproteomic Characterization of Mannosidase and Xylosyltransferase Mutant Strains of Chlamydomonasreinhardtii.
Plant Physiol 176: 1952-1964

Ozawa SI, Bald T, Onishi T, Xue H, Matsumura T, Kubo R, Takahashi H, Hippler M, Takahashi Y (2018)
Configuration of Ten Light-Harvesting Chlorophyll a/b Complex I Subunits in Chlamydomonas reinhardtii Photosystem I.
Plant Physiol 178: 583-595

Kosters M, Leufken J, Schulze S, Sugimoto K, Klein J, Zahedi RP, Hippler M, Leidel SA, Fufezan C (2018)
pymzML v2.0: introducing a highly compressed and seekable gzip format.
Bioinformatics 34: 2513-2514

Schulze S, Urzica E, Reijnders M, van de Geest H, Warris S, Bakker LV, Fufezan C, Martins Dos Santos VAP, Schaap PJ, Peters SA, Hippler M (2017)
Identification of methylated GnTI-dependent N-glycans in Botryococcus brauni.
New Phytol 215: 1361-1369

Leufken J, Niehues A, Sarin LP, Wessel F, Hippler M, Leidel SA, Fufezan C (2017)
pyQms enables universal and accurate quantification of mass spectrometry data.
Mol Cell Proteomics 16: 1736-1745

Esquivel RN, Schulze S, Xu R, Hippler M, Pohlschroder M (2016)
Identification of Haloferax volcanii Pilin N-Glycans with Diverse Roles in Pilus Biosynthesis, Adhesion, and Microcolony Formation.
J Biol Chem 291: 10602-14

Bald T, Barth J, Niehues A, Specht M, Hippler M, Fufezan C (2012)
pymzML--Python module for high-throughput bioinformatics on mass spectrometry data.
Bioinformatics 28: 1052-3

Specht M, Kuhlgert S, Fufezan C, Hippler M (2011b)
Proteomics to go: Proteomatic enables the user-friendly creation of versatile MS/MS data evaluation workflows.
Bioinformatics 27: 1183-4

Specht M, Stanke M, Terashima M, Naumann-Busch B, Janssen I, Hohner R, Hom EF, Liang C, Hippler M (2011a)
Concerted action of the new Genomic Peptide Finder and AUGUSTUS allows for automated proteogenomic annotation of the Chlamydomonas reinhardtii genome.
Proteomics 11: 1814-1823

Terashima M, Specht M, Naumann B, Hippler M (2010)
Characterizing the Anaerobic Response of Chlamydomonas reinhardtii by Quantitative Proteomics.
Mol Cell Proteomics 9: 1514-32

Allmer J, Kuhlgert S, Hippler M (2008)
2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments.
BMC Bioinformatics 9: 302

Allmer J, Naumann B, Markert C, Zhang M, Hippler M (2006)
Mass spectrometric genomic data mining: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii.
Proteomics 6: 6207-6220

Allmer J, Markert C, Stauber EJ, Hippler M (2004)
A new approach that allows identification of intron-split peptides from mass spectrometric data in genomic databases.
FEBS Lett 562: 202-6