All-atom de novo protein folding with a scalable evolutionary algorithm
Abhinav Verma
Institute for Scientific Computing, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Search for more papers by this authorSrinivasa M. Gopal
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Search for more papers by this authorJung S. Oh
Supercomputational Materials Lab, Korean Institute for Science and Technology, Seoul, Korea
Search for more papers by this authorKyu H. Lee
Supercomputational Materials Lab, Korean Institute for Science and Technology, Seoul, Korea
Search for more papers by this authorCorresponding Author
Wolfgang Wenzel
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, GermanySearch for more papers by this authorAbhinav Verma
Institute for Scientific Computing, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Search for more papers by this authorSrinivasa M. Gopal
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Search for more papers by this authorJung S. Oh
Supercomputational Materials Lab, Korean Institute for Science and Technology, Seoul, Korea
Search for more papers by this authorKyu H. Lee
Supercomputational Materials Lab, Korean Institute for Science and Technology, Seoul, Korea
Search for more papers by this authorCorresponding Author
Wolfgang Wenzel
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, Germany
Institute for Nanotechnology, Forschungszentrum Karlsruhe, Karlsruhe, GermanySearch for more papers by this authorAbstract
The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 Å in <24 h. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007
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