From sequence variability to protein (complex) structure prediction

Many families of homologous proteins show a remarkable degree of structural and functional conservation, despite the large variability in their amino acid sequences. We have developed a statistical-mechanics inspired inference approach to link this variability (easy to observe) to structure (hard to observe), i.e. to infer directly co-evolving residue pairs which turn our to form native contacts in the folded protein with high accuracy. The gained information is used to guide tertiary (and quaternary) structure prediction. As a specific example, I will discuss the auto-phosphorylation complex of histidine kinases, which are involved in the majority of signal transduction systems in the bacteria. Only a multidisciplinary approach integrating statistical genomics, biophysical protein simulation, and mutagenesis experiments, allows us to predict and verify the - so far unknown - active kinase structure