What is the PEPSI project?
PEPSI (Polynomial Expansions of Protein Structures and Interactions) is a 4-years project, supported by the ANR (French National Agency for Research) in the program Modèles Numériques (MN). The project started on the 01/11/2011. Its main goal is to develop novel algorithmic techniques for structural bioinformatics and apply these to current structural biology research projects.
Developing efficient ways to represent and manipulate the three-dimensional structures of protein molecules and to calculate reliably how large proteins interact are major challenges in computational biology. Developing new algorithms which can help the experimental determination of protein structures and which can help to predict how proteins interact at a structural level could offer immense scientific and therapeutic benefits. Currently, high throughput experimental techniques can identify protein-protein interactions on a genomic scale, but it remains extremely difficult to understand how these cellular components really work at the molecular level. Hence biological scientists now need to be able to compare and analyse the 3D structures of tens of thousands of protein molecules and to calculate hundreds of thousands of protein interactions on a routine basis. We believe the best way to meet these challenges is to exploit better the special mathematical properties of the classical special functions and to extend these approaches using new developments such as the Laplace-Beltrami eigenfunctions which have recently emerged from computational geometry.
Building on recent projects supported by previous ANR awards, this four-year project will bring together two teams of computational experts from INRIA Grenoble and INRIA Nancy with a group of experimental protein structure determination experts from the Jean-Pierre Ebel Institute of Structural Biology in order to develop novel algorithmic techniques for structural bioinformatics, and to apply these to current structural biology research projects. Although there are several theoretical aspects of this project, the primary aim is to develop new tools and algorithms which will be useful to many scientists working in biology, medicine, and pharmacology.
However, the mathematical techniques that will be developed will be rather generic, and could be useful in many areas that require 3D object recognition and retrieval such as medical imaging, virtual drug screening, and 3D human face recognition, for example. Hence improving our ability to identify novel protein-protein interactions and to perform fast and accurate correlations of protein shapes and other properties could be of significant value to the pharmaceutical industry and beyond.