Petra Gutenbrunner (MPG)
Petra Gutenbrunner is a PhD student at the Max Planck Institute of Biochemistry in Martinsried near Munich who works in the Computational Systems Biochemistry lab under the supervision of Prof. Jürgen Cox.
Before she began to study Biomedical Informatics, she worked more than six years as software engineer. During her studies, Petra held both Bachelor and Master internships at the world-renowned Wellcome Sanger Institute. There she had the privileged opportunity to acquire advanced skills in analyzing MS data. For her Bachelor’s thesis, she developed a workflow for accurate peptide identification in genomic applications. This workflow enabled refining the human genome annotation and thus novel gene identification. For her Master’s thesis, she developed a workflow to encompass all three of peptide identification, improved site-localisation of post-translational modifications and quantification in one software tool.
The identification and reconstruction of ancient proteins is challenging since they are highly modified and low abundant due to post-mortem decay. During her PhD, Petra will develop accurate tools to identify the still unknown sequences for cultural heritage material conservation, evolutionary studies, or unraveled pathways.
Petra's PhD project
Algorithm for the search engine driven and for the unrestricted identification of protein modifications will be developed for the specific challenges in ancient samples.
The development of specific computational algorithms will highly improve the identification rate of proteins in ancient samples leading to a better understanding of the production techniques and chemical preservation of cultural heritage materials.
Investigation of archaeological samples, when the target is species identification, can prove to be particularly challenging, as the genome of many non-domestic species used to produce everyday use objects, such as elk and deer for bone combs, or mink for elaborate fur garments, is still not available yet. Similarly, the streamlined identification of pathological variants, reported in publicly available protein databases, but commonly ignored in ordinary peptide-spectrum matching processes, could favour identifications of pathologies in ancient samples. In these cases reliable options for homology search, identification of sequence variants and amino acid substitutions, or de novo sequencing, integrated within the main search-engine would improve a lot the identification of cryptic, potentially species-diagnostic, peptides. Finally, improved algorithms for blind identification of spontaneous post-translational modifications will allow a better characterisation of the damage pattern affecting each sample, with direct relevance in conservation.
In the analysis of ancient proteins from cultural heritage material one faces several particular challenges that will all be addressed by development of software modules and algorithms in close collaboration with the experimental partners. These will be implemented as extensions to the established MaxQuant and Andromeda platforms. For several applications in ancient sample analysis, peptide identification from large search spaces are required. For this purpose the Andromeda search engine is further optimized to enable solid identification in this situation. Alternatively, pre-existing de novo sequencing techniques are further developed to be able to identify peptides and proteins independent of a sequence database. This includes clustering of single spectra de novo identifications in order to be able to confidently identify regions or domains of proteins that have a sufficient MS/MS sequence coverage, and to map out their modification content.
Algorithm for the search engine driven and for the unrestricted identification of protein modifications will be developed for the specific challenges in ancient samples. An expert system will be developed and trained to be able to automatically apply domain-specific rules for the interpretation of MS/MS spectra. This includes special rules for the proper treatment of deamidation and other modifications in order to aid distinguishing clear cases of ancient sample-specific processes from sample preparation by-products. An expert system-informed localization score will apply this knowledge to calculate reliable localization properties for PTMs. Furthermore a library will be built up with the information obtained on ancient peptides with and without PTMs in order to be able to apply this knowledge to new samples and to improve the dynamic range of detection in these.
The development of Top-down paleoproteomics is supported by the development of a Top-Down version of MaxQuant that will be able to identify and quantify proteins from relatively complex mixtures and the determination of their PTMs from MS/MS spectra acquired with a variety of fragmentation types. This will be done in close collaboration with Patrick Rüther (@UCPH) and Diana Samodova (@UCPH).
Secondment period of 6 months at UCPH (JVO co-supervision) to collaborate with Georgia Ntasi (@UNINA) to test on Patrick Rüther's (@UCPH) datasets the developed algorithms.
Secondment period of 4 months at Thermo (Bremen) to develop and test new control software for the Orbitrap Fusion Lumos Tribrid mass spectrometer.
10/2014 – 09/2016 MSc in Biomedical Informatics (passed with highest distinction) at the University of Applied Sciences Upper Austria, Hagenberg Campus
10/2015 – 07/2016 Master’s thesis at Wellcome Sanger Institute, Proteomic Mass Spectrometry
Title: A Computational Proteomics Pipeline for Phosphorylation Site Localisation
10/2012 – 09/2014 BSc in Medical and Bioinformatics (passed with distinction) at the University of Applied Science Upper Austria, Hagenberg Campus
03/2014 – 08/2014 Bachelor’s thesis at Wellcome Sanger Institute, Proteomic Mass Spectrometry
Title: Comparison of Database Search Strategies for Mass Spectrometry Data Analysis
03/2014 – 08/2014 Theoretical Bachelor’s Thesis: Implementation of “Alu-Finder”:
Transposable elements are sequences in the genome that appear more frequently. Alu elements are a group of these elements and are more abundant in gene-rich regions of the genome, thus, can influence gene expression. I developed the software Alu-Finder with the aim to encompass the approaches: identification of Alu elements in a sequence, select already identified elements from a database, and calculate the similarity between sequences and is illustrated using phylogenetic trees.
10/2011 Awarded title “Ingenieurin” (“Ing.”) from Federal Ministry of Economy, Families and Youth (bmwfi)
08/2006 – 09/2013 Full-time Software engineer/Project leader Softpoint electronic GmbH & Co KG
The ESR has completed 1 month of secondment at UCPH in the laboratory of Prof. Enrico Cappellini, where she tested and fine-tuned the computational reconstruction of ancient protein sequences. The ESR has also completed 2 months of secondment at the same laboratory, where she extended the computational reconstruction of ancient protein sequences.
Enrico Cappellini, Ana Prohaska, Fernando Racimo, Frido Welker, Mikkel Winther Pedersen, Morten Allentoft, Peter de Barros Damgaard, Petra Gutenbrunner, Julie Dunne, Simon Hammann, Mélanie Roffet-Salque, Melissa Ilardo, J. Víctor Moreno-Mayar, Yucheng Wang, Martin Sikora, Lasse Vinner, Jürgen Cox, Richard P. Evershed, and Eske Willerslev (Annual Review of Biochemistry, 2018)
Pavel Sinitcyn, Shivani Tiwary, Jan Rudolph, Petra Gutenbrunner, Christoph Wichmann, Sule Yilmaz, Hamid Hamzeiy, Favio Salinas, and Jürgen Cox (2018)
Weisser H, Wright JC, Mudge JM, Gutenbrunner P, Choudhary JS. (J Proteome Res. 2016 Dec 2;15(12):4686-4695. Epub 2016 Nov 10.)
Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D, Wolski WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O. (Nat Methods. 2016 Aug 30;13(9):741-8. doi: 10.1038/nmeth.3959.)
Frido Welker, Jazmín Ramos-Madrigal, Petra Gutenbrunner, Meaghan Mackie, Shivani Tiwary, Rosa Rakownikow Jersie-Christensen, Cristina Chiva, Marc R. Dickinson, Martin Kuhlwilm, Marc de Manuel, Pere Gelabert, María Martinón-Torres, Ann Margvelashvili, Juan Luis Arsuaga, Eudald Carbonell, Tomas Marques-Bonet, Kirsty Penkman, Eduard Sabidó, Jürgen Cox, Jesper V. Olsen, David Lordkipanidze, Fernando Racimo, Carles Lalueza-Fox, José María Bermúdez de Castro, Eske Willerslev & Enrico Cappellini (April 2020)
Tiwary S*, Levy R*, Gutenbrunner P*, et al. Nat Methods. (2019)