Section: New Results
Modeling Interfaces and Contacts
Assessing the Stability of Protein Complexes within Large Assemblies
Keywords : Tandem Affinity Purification, large protein assemblies, data integration, curved Voronoi diagrams, topological stability.
Participants : Frédéric Cazals, Tom Dreyfus.
Structural genomics projects, in particular those exploiting Tandem Affinity Purification (TAP), have revealed remarkable features of full proteomes. While these insights are essentially of combinatorial nature, that is a number of proteins are known to interact within a complex, leveraging this information will require building three dimensional models of these assemblies. Such an endeavour has recently been completed for the Nuclear Pore Complex, for which plausible reconstructions have been computed from different experimental data, including TAP data. But the reconstruction is qualitative and the coherence to TAP data is not analyzed.
In this work [16] , we introduce toleranced collections of balls to represent protein assemblies known with uncertainties, together with a method highlighting stable complexes within such assemblies. The method relies on the computation of the topological stability of the connected components in a collection of balls growing according to a so-called additively-multiplicatively-weighted Voronoi diagram. In particular, our strategy enables the investigation of the coherence between a reconstructed model and TAP data.
Comparing Voronoi and Laguerre tessellations in the protein-protein docking context
Keywords : Protein-protein interactions, docking, learning, Voronoi and Laguerre tessellations.
Participant : Julie Bernauer.
In collaboration with Thomas Bourquard, Jerome Azé, and Anne Poupon.
T. Bourquard is with LRI Université Paris-Sud 11.
J. Azé is with LRI Université Paris-Sud 11.
A. Poupon is in the Physiologie de la Reproduction et des Comportements lab, INRA Tours.
Most proteins fulfill their functions through the interaction with other proteins. Because most of these interactions are transitory, they are difficult to detect experimentally, and obtaining the structure of the complex is generally not possible. Consequently, prediction of the existence of these interactions and of the structure of the resulting complex has received a lot of attention in the last decade. However, proteins are very complex objects, and classical computing methods have led to computer-time consuming methods, whose accuracy is not sufficient for large scale exploration of the so-called “interactome”, the ensemble of protein-protein complexes in the cell. In order to design an accurate and high-throughput prediction method for protein-protein docking, the first step was to model a protein structure using a formalism amenable to fast computation, without losing the intrinsic properties of the object. In our work [14] , we have tested two different, but related, formalisms: the Voronoi and Laguerre tessellations. We present here a comparison of these two models in the context of protein-protein docking.
A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation
Keywords : Knowledge-based potential, structure prediction and refinement, spherical arrangements, surface area, coarse-grained model.
Participants : Julie Bernauer, Frédéric Cazals, Sébastien Loriot.
In collaboration with M. Levitt, Dpt of Structural Biology, Stanford University.
Knowledge-based protein folding potentials have proven successful in the recent years. Based on statistics of observed interatomic distances, they generally encode pairwise contact information.
In this study [15] , we present a method that derives multi-body contact potentials from measurements of surface areas using coarse-grained protein models. The measurements are made using a newly implemented geometric construction: the arrangement of circles on a sphere [13] . This construction enables the definition of residue covering areas which are used as parameters to build functions able to distinguish native structures from decoys. These functions, encoding up to 5-body contacts are evaluated on a reference set of 66 structures and its 45000 decoys, and also on the often used lattice_ssfit set from the decoys'R us database. We show that the most relevant information for discrimination resides in 2- and 3-body contacts. The potentials we have obtained can be used for evaluation of putative structural models; they could also lead to different types of structure refinement techniques that use multi-body interactions.
