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Overview

Proteins are the core components of all life processes and thus play central roles in biological mechanism and disease. Over billions of years a seemingly limited repertoire of protein types has evolved to produce an enormous diversity, with some 25 000 distinct proteins in humans and millions throughout nature. Understanding how their evolution is coupled to their function: how they interact with other molecules, form larger molecular machines, and ultimately create the vast complex networks inside of cells is critical for our deeper understanding of how living systems function, or malfunction pathologically.

Our group is primarily interested in deciphering the mechanisms by which proteins recognise other molecules and ultimately to combine these mechanisms into models of molecular machines, pathways and larger biological systems. Central to this is our work on predictive networks, where we use biological networks to predict a variety of biological phenomena from phosphorylation events, to chemical toxicity in humans. We adopt a number of computational methods, coupled to laboratory experiments to deduce mechanism, and are very active in collaborations with other groups in the Heidelberg area, and the rest of the world. We develop and apply bioinformatics methods and numerous databases during the course of our work. We also run a small laboratory where we mostly perform biochemistry and biophysics approaches to test various predictions. Our collaborators include other computational scientists, molecular biologists, structural biologists, proteomics specialists, cell biologists, geneticists, chemists and many others.

A full list of publications can be found here. Current projects are focused in the following main areas.

Protein interaction and complex modeling

Protein interaction networks are central to any understanding of cellular processes, and though many thousands are now known, few initiatives to uncover them pay much attention to one of the best sources of data available: complexes of known 3D structure. We thus study protein interactions by considering known 3D structures. We use 3D complexes to interrogate interactions identified by other methods (e.g. yeast two-hybrids) and to predict specific interactions within protein families. A major initiative in the group is related building as complete models as possible for all interacting proteins and complexes in a whole cell (see Figure). This is particularly useful when combined with experimental methods like electron microscopy, cryo-electron tomography, mass-spectrometry or proteome-scale interaction discovery. We have also participated in attempts to decipher the entire interacting proteome within organisms.

Aloy et al, Science 2004

Network depicting modellable complexes in Yeast (see Aloy et al, Science 2004).

Kuehner et al, Science 2009

Fitting modelled complexes into a tomogram of Mycoplasma pneumoniae (see Kuehner et al, Science 2009).

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Protein-peptide interactions & motifs

A major current challenge in biology is to discover and understand short protein tretches that mediate functional interactions. We have developed several methods for predicting these kinds of interactions, and continue to apply them to new problems in biology.

DiLiMOT

Schematic outlining DiLiMOT: our appraoch for finding protein linear motifs that mediate protein-protein interactions. See Neduva et al, PLoS Biol. 2005).

PepSite

Schematic detailing PepSite: our approach for finding peptide binding sites on protein surfaces. See Petsalaki et al, PLoS Comp. Biol 2009)

For more information see: