Fachbereich Informatik - Aktuell

Disputation Timo Sachsenberg

am Dienstag, 24. April 2018 um 8:30 Uhr in Raum F 122, Sand 6

Timo Sachsenberg

Computational Methods for Mass Spectrometry-based Study of Protein-RNA/DNA Complexes and Quantitative Metaproteomics

Berichterstatter 1: Prof. Dr. Oliver Kohlbacher
Berichterstatter 2: Prof. Dr. Knut Reinert
Berichterstatter 3: Prof. Dr. Lukas Käll

In the last decade, the use of high-throughput methods has become increasingly popular in various fields of life sciences. Today, a wide range of technologies allow gathering detailed quantitative insights into biological systems. Computational mass spectrometry is a research field in bioinformatics that collects and analyzes data from mass-spectrometric high-throughput experiments.
The focus of this doctoral defense talk are two new methods for computational mass spectrometry that were developed as part of the thesis. The first method applies to a scientific problem from the field of structural biology: to determine spatial interactions between protein and nucleic acids. For this purpose, we developed experimental protocols, programs, and analysis workflows that allow identifying UV-induced cross-links in (ribo-)nucleoprotein complexes from mass spectrometry data. An outstanding feature of our method is the ability to exactly localize amino acids and nucleotides in contact with each other. Applied to data from yeast and human we identify new interaction partners with, to date, unmatched resolution. The second method enables metaproteomic studies of complex communities of microorganisms. Organisms greatly differ in their biochemical repertoire allowing them to decompose a wide range of substrates. Remarkably, some soil bacteria even nourish themselves from environmental toxins. To determine these organisms, we feed substrates labeled with stable isotopes to mark organisms that process the substrate. The isotope abundances in proteins extracted from these organisms then serve as proof for substrate metabolism. These isotope abundances are automatically determined by our novel computational method and assigned to individual organisms. Our approach allows identifying organisms involved in degradation processes and grouping them according to their function. The developed algorithms and automation of the analysis process reduces the manual work from several months to a few minutes and enables, for the first time, large study sizes in protein-based stable isotope probing experiments.
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