Abstract

Significant progress has been recently achieved in the prediction of protein structures, due to advances such as AlphaFold, RosettaFold and in general deep-learning based methods. These tools are revolutionizing the field of structural biology by providing reliable predictions of protein 3D structures. Despite these developments, predicting protein dynamics remains a formidable challenge due to the inherent complexity of dynamic motions of proteins and the scarcity of experimental data on alternative protein conformational states. Detecting and characterizing protein conformational changes on a large scale would be however very relevant as they can profoundly alter protein function and activity. In my talk, I will present a mass spectrometric method that enables in situ analyses of protein conformational changes for thousands of proteins simultaneously and across conditions. The approach probes dynamic alterations of protein structures due to post-translational modifications, binding of other molecules, cleavages and protein aggregation events. Such a structural ‘omics readout, combined with predicted or experimental protein structural data, enables screening for pathological or physiological protein conformations from cells and tissues, supports the generation of mechanistic hypotheses and bridges system and molecular views. I will present applications of this approach to the in situ detection of drug-target interactions and the identification of a novel class of structural disease biomarkers.