Mathias
Wilhelm

Mathias Wilhelm is a trained bioinformatician but generally intrigued by all mechanisms which require a good understanding of mathematics, physics, chemistry, and biology. Initially working in the field of metabolomics, he spent the last 7 years of his scientific career focusing on studying the life-cycle of proteins using mass spectrometry. Today, he leads a small group of bioinformaticians at the Chair of Proteomics and Bioanalytics (Prof. Bernhard Kuster, Technical University Munich) and is interested in supporting wet-lab scientists by developing tools for data analysis, integration, interpretation, and dissemination. He is also a co-founder of OmicScouts GmbH and MSAID GmbH, both providing services in the field of mass spectrometry-based proteomics.

Mathias Wilhelm heads the Bioinformatics group at the Chair of Proteomics and Bioanalytics at the TUM School of Life Sciences. Mathias Wilhelm obtained his Ph.D. at the Chair of Proteomics and Bioanalytics, developing and implementing ProteomicsDB, a large-scale in-memory database hosting a first draft of the human proteome. Before this, he studied Bioinformatics (B.Sc.) and Informatics in the natural sciences (M.Sc.) in Bielefeld. He spent about 1 year abroad for his master thesis and a position as a research assistant at the Boston Children’s Hospital in Hanno Steen’s lab. He co-founded two spinoffs and published more than 35 papers. His recent interests include, but are not limited to, the application of deep learning in proteomics to improve data analysis and interpretation.

The chair of Proteomics and Bioanalytics develops novel analytical methods for quantitative proteome research, elucidates the selectivity of drug-target interactions on a proteome-wide scale, investigates the cellular mechanisms by which drugs exert their function, as well as indicators of drug effectiveness and treatment response. For this, a wide range of bioinformatics tools are required. The two major bioinformatics flagship projects are ProteomicsDB, which offers open access and analytical tools for the investigation of multiple omics data from multiple organisms, and Prosit, a deep learning architecture allowing the prediction of many peptide properties. Prosit was initially developed to predict peptide fragment ion intensities. However, its generic architecture allowed the training and prediction of other peptide properties, such as retention time and proteotypicity.