Predictor@Home: A "protein structure prediction supercomputer" based on global computing
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.
global computing paradigm public resources protein conformational sampling monte carlo simulations molecular dynamics