BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
Evaluating protein structure-prediction schemes using energy landscape theory
IBM Journal of Research and Development
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
DGMonitor: A Performance Monitoring Tool for Sandbox-Based Desktop Grid Platforms
The Journal of Supercomputing
Predicting vertebrate promoters using heterogeneous clusters
International Journal of Ad Hoc and Ubiquitous Computing
Constructing a p2p-based high performance computing platform
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Distributing evolutionary computation in a networked multi-agent environment
Mathematical and Computer Modelling: An International Journal
Grid and distributed public computing schemes for structural proteomics: a short overview
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
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Predicting the structure of a protein from its amino acid sequence is a complex process the understanding of which could be used to gain new insight into the nature of protein function or provide targets for structure-based design of drugs to treat new and existing diseases. While protein structures can be accurately modeled using computational methods based on all atom physics-based force fields including implicit solvation, these methods require extensive sampling of native-like protein conformations for successful prediction, and consequently they are often limited by inadequate computing power. To address this problem, we developed Predictor@Home, a "structure prediction supercomputer" powered by the Berkeley Open Infrastructure for Network Computing (BOINC) framework and based on the public-resource computing paradigm (i.e., volunteered computing resources interconnected to the Internet and owned by the public). In this paper, we describe the protocol we employed for protein structure prediction and the integration of these methods into a public-resource architecture. We show how Predictor@Home significantly improved our ability to predict protein structureby increasing our sampling capacity by 1-2.5 orders of magnitude.