BPCPPDYSOE21 – scientific programme

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BP: Fachverband Biologische Physik

BP 21: Systems Biology III

BP 21.1: Invited Talk

Tuesday, March 23, 2021, 14:00–14:30, BPa

Predicting Protein and RNA Structures: from statistical physics 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. For proteins, systematic large-scale studies of >1000 protein families are already possible. Additional information, such as low-resolution experimental information (e.g. SAXS or FRET) can be used as further constraints in simulations. For RNA there are significantly less data available, which hinders in particular ML based approaches. Still, DCA combined with ML can improve prediction quality.