Section: Application Domains

Structural protein analysis on Nimbus clouds

Proteins are major components of the life. They are involved in lots of biochemical reactions and vital mechanisms for living organisms. The three-dimensional (3D) structure of a protein is essential for its function and for its participation to the whole metabolism of a living organism. However, due to experimental limitations, only few protein structures (roughly, 60,000) have been experimentally determined, compared to the millions of proteins sequences which are known. In the case of structural genomics, the knowledge of the 3D structure may be not sufficient to infer the function. A usual way to make a structural analysis of a protein or to infer its function is to compare its known, or potential, structure to the whole set of structures referenced in the Protein Data Bank (PDB).

In the framework of the MapReduce ANR project led by KerData, we focus on the SuMo application (Surf the Molecules) proposed by Institute for Biology and Chemistry of the Proteins from Lyon (IBCP, a partner in the MapReduce project). This application performs structural protein analysis by comparing a set of protein structures against a very large set of structures stored in a huge database. This is a typical data-intensive application that can leverage the Map-Reduce model for a scalable execution on large-scale distributed platforms. Our goal is to explore storage-level concurrency-oriented optimizations to make the SuMo application scalable for large-scale experiments of protein structures comparison on cloud infrastructures managed using the Nimbus IaaS toolkit developed at Argonne National Lab (USA).

If the results are convincing, then they can immediately be applied to the derived version of this application for drug design in an industrial context, called MED-SuMo, a software managed by the MEDIT SME (also a partner in this project). For pharmaceutical and biotech industries, using a cloud computing facility unlocks several new applications for drug design. Rather than searching for 3D similarity into biostructural data, it will become possible to classify the entire biostructural space and to update all derivative predictive models periodically with new experimental data. The applications in this complete chemo-proteomic vision address the identification of new druggable protein targets, and thereby the generation of new drug candidates.