After 53,000 years of computing power, phase 2 of Help Cure Muscular Dystrophy is ending. It's good to see detailed feedback from the researchers on what our crunching efforts are helping to accomplish.

From the WCG site:

World Community Grid is pleased to announce, that as a result of the generous contribution of computing power from our members, the Help Cure Muscular Dystrophy - Phase 2 project is very close to being completed.

The Help Cure Muscular Dystrophy - Phase 2 project was launched on May 12, 2009. To date, World Community Grid members have processed over 113 million results which required nearly 53,000 years of computing power. This work would have taken too many years to even be attempted, using the computing resources available to the researchers at the Université Pierre et Marie Curie and joint research facilities. Using World Community Grid, this research was completed in less than 2.5 years.

The researchers are now working on analyzing the results data to determine the more detailed protein-to-protein interactions involved with neuromuscular diseases. They expect to publish their results in public databases, along with descriptive papers.

You may read about these plans in this forum post by Dr. Alessandra Carbone, the lead researcher on the Help Cure Muscular Dystrophy - Phase 2 project.

If you contributed your computer power to the Help Cure Muscular Dystrophy - Phase 2 research project, the staff at the Université Pierre et Marie Curie in Paris wish to express their sincere gratitude to you.

From the lead researcher:

We are at the end of HCMD2 and I would like to thank you for the patience and persistence in running our docking program on your machines. The huge amount of cross-docking data that we collected, thanks to you (!), has been realized for the first time. It is a mine of information for our research in protein-protein interactions and it will also constitute a precious amount of information for our colleagues around the world interested in molecular docking.

We finished analyzing the data on the 168 protein complexes run on HCMD1 and we now know what has to be done next. We shall integrate novel and quantitative, experimental data on protein binding to predict not only the conformation of interacting proteins, but also which proteins will interact and how strongly. This involves four specific challenges:

1) Obtain quantitative experimental data on protein interactions with a wide range of binding affinities. We will use surface plasmon resonance (SPR), followed by isothermal titration calorimetry (ITC) to fully characterize the thermodynamics of protein interactions over a wide range of affinities and physical conditions (concentration, pH, temperature, …). These methods constitute ideal tools for our purpose. They will be used to quantify interactions between a set of commercially available proteins, including known interacting partners. However, we will also characterize nominally non-functional "cross-interactions" within this set to test, for the first time, the common assumption that choosing single proteins from known binary complexes, or choosing proteins from different cellular compartments, implies the absence of interaction.

2) Use evolutionary sequence data to detect protein residues involved in interaction interfaces and pairs of interacting proteins. We will identify key residues within interaction sites and co-evolution signals between pairs of interaction sites in order to predict interacting partners and integrate this information into a refined molecular docking approach, with the aim of identifying binary interactions within a large set of proteins. This goal will include constructing an automated pipeline for co-evolution analysis of single proteins and protein pairs.

3) Formulate new protein-protein interaction potentials using experimental data, molecular simulations and existing structural data. Molecular simulations coupled with free energy calculations will be used to obtain an atomic-scale view of the dissociation of a limited number of the weak and strong protein interactions studied by microcalorimetry. We will determine the extent to which complexes have well-defined conformations and fully desolvated interfaces. This data will be used to formulate and iteratively refine new interaction potentials within a coarse-grain model, which will be sensitive to binding affinity.

4) Carry out a refined analysis of the large database of protein interactions that you generated to characterize interaction networks and binding promiscuity. During stage two of the Help Cure Muscular Dystrophy project (HCMD2), the resources of World Community Grid were used to dock all possible protein pairs within a set of 2200 proteins, potentially important for understanding and treating neurodegenerative diseases. This data will be analyzed to characterize key “hub” proteins and network structures, first, with the existing energetic and residue conservation data and then with the new methods resulting from 1-3.

The methods and interaction data derived from our studies will be freely available to the scientific community by the implementation of web servers and web databases.

We will do all this with 4 years funding from the French ministry of research that was awarded to our group this year. We shall devote this grant to the development of the new tools (in biophysics and bioinformatics) mentioned above, as well as on the analysis of the HCMD2 dataset to arrive to the best prediction possible on the human protein-protein interaction network that you generated in these last two years.

To keep you informed on the development of the project, I shall provide news on the advancements in my webpage. Pointers to the publications will be given there. If by any chance I do not post news from more than 6 months, send me a reminder!

THANK YOU again to all of you from all the scientists of the HCMD1 and HCMD2 projects.