Frank Sobott

Professor Frank Sobott is Chair of Biomolecular Mass Spectrometry in the School of Molecular and Cellular Biology at the University of Leeds, Faculty of Biological Sciences. His research focuses on developing and applying cutting-edge mass spectrometry methods to study dynamic protein structures and complexes in near-native environments.

His research interests lie at the intersection of biology, chemistry, and physics, particularly in native mass spectrometry, ion mobility, hydrogen-deuterium exchange (HDX), chemical crosslinking, and covalent labeling techniques such as Fast Photochemical Oxidation of Proteins (FPOP). These are integrated with cryo-electron microscopy and computational modeling to achieve high-resolution structural insights.

The research program emphasizes:

• Protein conformation and aggregation, especially amyloid formation linked to neurodegenerative diseases
• Structural analysis of membrane proteins and their interactions with lipids using nanodiscs, SMALPs, and amphipols
• Time-resolved studies of protein dynamics
• Top-down characterization of proteins and complexes
• Structural profiling of biopharmaceuticals
• Investigating intrinsically disordered proteins and their role in disease

Although specific publications and student advisees are not listed in the source text, his group develops novel experimental and computational tools to bridge in vitro and in vivo structural biology, aiming to understand molecular mechanisms in health and disease. Collaborations span the Astbury Centre for Structural Molecular Biology, UK research networks, international institutions, and biopharmaceutical industry partners.

Scientific awards and honors are not mentioned in the provided material.

He leads a research team working on integrating structural biology techniques to uncover molecular details of complex biological systems. The lab is equipped for advanced mass spectrometry, including nano-electrospray ionization, high-resolution ion mobility, and Electron Transfer Dissociation (ETD) for conformational mapping and interaction site analysis. Future work aims to extend structural insights into live cellular contexts using covalent labeling strategies and machine learning-enhanced data interpretation.