Dresden 2020 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 91: Computational Biophysics (joint session BP/CPP)

CPP 91.7: Invited Talk

Thursday, March 19, 2020, 11:30–12:00, SCH A251

Predicting Protein and RNA Structures via data inference: from Potts models to machine learning — •Alexander Schug — John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich — Faculty of Biology, University of Duisburg-Essen

On the molecular level, life is orchestrated through an interplay of many biomolecules. To gain any detailed understanding of biomolecular function, one needs to know their structure. Yet the structural characterization of many important biomolecules and their complexes - typically preceding any detailed mechanistic exploration of their function- remains experimentally challenging. Tools rooted in statistical physics such as Direct Coupling Analysis (DCA) but also increasingly Machine Learning driven approaches take advantage of the explosive growth of sequence databases and infer residue co-evolution to guide structure prediction methods via spatial constraints. Going beyond anecdotal cases of a few protein families, systematic large-scale studies of >1000 protein families are now possible and other information, such as low-resolution experimental information (e.g. SAXS or FRET) can be used as further constraints in simulations.