Using genetic algorithms to predict structural protein-protein interactions. Most proteins fulfill their functions through the interaction with one or many partners as nucleic acids, 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, "in silico 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 approaches have lead to computer-time consuming methods, whose accuracy is not sufficient for large scale exploration of the so-called "interactome" of different organisms. In this context development of high-throughput prediction methods for protein-protein docking is needed. We present here the implementation of a new method based on: Two types of formalisms: The Voronoi and Laguerre tessellations Two simplified geometric models for coarse-grained modeling of complexes. This leads to computation time more reasonable than in atomic representation. The use and optimization of learning algorithms (genetic algorithms) to isolate the most relevant conformations between two protein partners. An evaluation method based on clustering of meta-attributes calculated at the interface to sort the best subset of candidate conformations.
