Tobias Schmidt is a fourth-year Ph.D. candidate at the chair of Proteomics and Bioanalytics of Prof. Kuster in Freising, Technical University Munich, Germany. Before coming to the Technical University of Munich for his MSc in molecular biotechnology he spent three years at the Karlsruhe Institute of Technology studying mathematics.
His research interest is in in-memory databases (ProteomicsDB), combining modern machine learning with high quality synthetic and real-world data as well as porting legacy (academic) systems to new technologies. His doctoral thesis work explores among other things the usage of his prediction tools for data analysis pipelines requiring high-quality spectral libraries.
Setting up and analyzing MRM/PRM assays require prior information on the retention times and fragmentation spectra of peptides. For so far unobserved peptides, synthetic peptides can be used to obtain these characteristics, however, are costly with uncertain results. We propose a novel cost-efficient approach which utilized our deep learning framework Prosit for the generation of in-silico spectral libraries with near reference data quality for virtually any peptide on a proteome-wide scale.
Read More Skyline-compatible libraries can be generated via ProteomicsDB on-demand and thus allow an initial screening of any peptide of interest. We demonstrate this approach on a dataset which was successfully acquired and analyzed using a predicted library. Subsequently, detected peptides were validated using synthetic peptides. Furthermore, existing libraries can be optimized by using Prosit’s CE-dependent predictions to weaken or boost specific fragments. Thus, predicted libraries supplement prior information and enable the investigation of unobserved proteins and peptides.
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