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
SimBA: A Discrete Event Simulator for Performance Prediction of Volunteer Computing Projects
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
A survey of desktop grid applications for e-science
International Journal of Web and Grid Services
A distributed evolutionary method to design scheduling policies for volunteer computing
Proceedings of the 5th conference on Computing frontiers
A distributed evolutionary method to design scheduling policies for volunteer computing
ACM SIGMETRICS Performance Evaluation Review
Modeling Job Lifespan Delays in Volunteer Computing Projects
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Future Generation Computer Systems
Comparison of parallel multi-objective approaches to protein structure prediction
The Journal of Supercomputing
Concurrency and Computation: Practice & Experience
| Hi-index | 0.00 |
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 functions 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 global 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 its integration into a global computing architecture based on public resources. We show how Predictor@Home significantly improved our ability to predict protein structures by increasing our sampling capacity by one to two orders of magnitude.