Berlin 2018 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 12: Focus Session: Statistical Physics-Based Methods in Molecular Evolution - organized by Alexander Schug and Martin Weigt (joint session BP/DY)

DY 12.2: Talk

Monday, March 12, 2018, 15:30–15:45, H 2013

Big Data in Structural Biology: Predicting Protein and RNA Structures by inferring residue co-evolution — •Alexander Schug — John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich

To gain any detailed understanding of biomolecular function, one needs to know their structure. The structural characterization of many important biomolecules and their complexes remains experimentally challenging. Novel statistical tools based on statistical physics such as Direct Coupling Analysis (DCA) take advantage of the explosive growth of sequential databases and trace residue co-evolution to infer secondary and tertiary contacts for proteins [1] and RNAs [2]. These contacts can be exploited as spatial constraints in structure prediction methods leading to excellent quality predictions [1,2,3]. Going beyond anecdotal cases of a few protein families, we have applied our methods to a systematic large-scale study of nearly 2000 PFAM protein families of homo-oligomeric proteins [4]. Also, we can apply DCA to infer mutational landscapes by capturing epistatic couplings between residues and can assess the dependence of mutational effects on the sequence context where they appear [5].

[1] Weigt M et al., PNAS (2009); F. Morcos et al., PNAS (2011)

[2] E. De Leonardis et al., NAR (2015)

[3] Schug A et al., PNAS (2009); Dago A et al., PNAS (2012)

[4] G. Uguzzoni et al., PNAS (2017)

[5] M. Figliuzzi et al., MBE (2016)