Wikipedia
Rosetta@home is a distributed computing project for protein structure prediction on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, run by the Baker laboratory at the University of Washington. Rosetta@home aims to predict protein–protein docking and design new proteins with the help of about sixty thousand active volunteered computers processing at over 210 teraFLOPS on average as of July 29, 2016. Foldit, a Rosetta@Home videogame, aims to reach these goals with a crowdsourcing approach. Though much of the project is oriented towards basic research on improving the accuracy and robustness of the proteomics methods, Rosetta@home also does applied research on malaria, Alzheimer's disease and other pathologies.
Like all BOINC projects, Rosetta@home uses idle computer processing resources from volunteers' computers to perform calculations on individual workunits. Completed results are sent to a central project server where they are validated and assimilated into project databases. The project is cross-platform, and runs on a wide variety of hardware configurations. Users can view the progress of their individual protein structure prediction on the Rosetta@home screensaver.
In addition to disease-related research, the Rosetta@home network serves as a testing framework for new methods in structural bioinformatics. These new methods are then used in other Rosetta-based applications, like RosettaDock and the Human Proteome Folding Project, after being sufficiently developed and proven stable on Rosetta@home's large and diverse collection of volunteer computers. Two particularly important tests for the new methods developed in Rosetta@home are the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and Critical Assessment of Prediction of Interactions (CAPRI) experiments, biannual experiments which evaluate the state of the art in protein structure prediction and protein–protein docking prediction, respectively. Rosetta@home consistently ranks among the foremost docking predictors, and is one of the best tertiary structure predictors available.