Developing new tools for proteome-wide cross-linking mass spectrometry

• Applicant: Professorin Dr. Fan Liu
• Subject Area: Biochemistry
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 495949291

Cross-linking mass spectrometry (XL-MS) can characterize protein structures and protein-protein interactions (PPIs) at proteome scale, but many of its data generation and analysis approaches are still lagging behind traditional proteomics. Notably, proteomics routinely relies on machine learning classifiers to improve peptide identification and tandem mass tag (TMT)-based peptide labelling to quantify condition-dependent proteome changes, but such strategies are missing for proteome-wide XL-MS. Here, I propose to develop a more sensitive and robust proteome-wide XL-MS workflow by establishing a machine learning-based strategy for PPI identification and a TMT-based quantitative XL-MS method compatible with complex biological samples. To improve PPI assignment, I will generate a fully controlled PPI dataset consisting of hundreds of known pairs of purified proteins. To establish quantitative XL-MS, I will design benchmarking experiments to systematically optimize the MS acquisition process towards highly sensitive cross-link identification and quantification. I will apply the new quantitative XL-MS workflow to characterize changes of mitochondrial protein structures and PPIs in a mouse model of Leigh syndrome, a common pediatric mitochondrial disease. Collectively, this research project will provide methods enabling more rigorous and biologically relevant proteome-wide XL-MS experiments and a resource of disease-associated alterations of the endogenous mitochondrial interactome.

• DFG Programme: Research Grants