All-atom de novo protein folding with a scalable evolutionary algorithm.
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 A in < 24 h.
Verma
Abhinav
A
Institute for Scientific Computing, Forschungszentrum Karlsruhe, Karlsruhe, Germany.
Gopal
Srinivasa M
SM
Oh
Jung S
JS
Lee
Kyu H
KH
Wenzel
Wolfgang
W
D000465:Algorithms; D003198:Computer Simulation; D054298:Human Immunodeficiency Virus Proteins / Q000737:chemistry*; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding
D054298:Human Immunodeficiency Virus Proteins