Bio
After her PhD at the University of Padua (Italy), Paola Picotti did postdoctoral research in the group of Ruedi Aebersold at ETH Zurich, where she developed targeted proteomic technologies based on mass spectrometry. In 2011, she was appointed Assistant Professor at the Institute of Biochemistry, ETHZ, and in 2017 tenured Associate Professor at the Institute of Molecular Systems Biology, ETHZ. Major contributions of the Picotti group include the development of structural proteomics technologies to probe in situ protein structural changes, characterization of the determinants of proteome thermostability, large-scale identification of protein-small molecule interactions, and the discovery of regulators of toxic proteins in Parkinson’s disease. Dr. Picotti was awarded the Latsis Prize, the Cotter Award of US HUPO, the SGMS award, the EMBO Young Investigator Award, the Friedrich Miescher Award, the Juan- Pablo Albar award of the European Proteome Association, ERC Starting and Consolidator grants, and the EMBO Gold Medal.
About her talk: Proteomes in 3D: Structural barcodes to probe protein functional alterations
Proteomics has been broadly applied to detect changes in protein levels in response to perturbations and derive information on altered pathways. Beyond protein expression changes, however, biological processes are also regulated by events such as intermolecular interactions, chemical modification and conformational changes. These events do not affect protein levels and therefore escape detection in classical proteomic screens. I will present how a global readout of protein structure can detect various types of protein functional alterations concomitantly. The approach, relying on the LiP-MS technique, monitors structural changes in thousands of proteins simultaneously in situ. It captures enzyme activity changes, allosteric regulation, phosphorylation and protein complex formation and pinpoints regulated functional sites, thus substantially expanding the coverage of proteomic analyses and supporting the generation of mechanistic hypotheses. Applications of this approach include the study of complex phenotypes, the identification of disease biomarkers and drug target deconvolution.
