A paper about the first phase of the Help Cure Muscular Dystrophy project and its initial results was published in the journal “PLOS Computational Biology” on December 5, 2013, Volume 9, Issue 12.
"Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information"
Lay Person Abstract:
Understanding the relationship among proteins in a cell is an important key to understanding the life processes within cells and consequently how diseases manifest themselves. The researchers for the first phase of the Help Cure Muscular Dystrophy project have published a paper about their initial findings regarding the physical interactions of 168 proteins, used to validate their approach to be applied to the second phase of their project dealing with proteins associated with neuromuscular diseases such as muscular dystrophy. They have published their analysis of protein-to-protein interactions, the methods used and have provided data and software to other scientists on their web site. This was all possible with the help of World Community Grid members who contributed their spare computer time to these calculations.
Technical Abstract:
Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions.
Access to Paper:
To view the paper, please click here.