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 comp...
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Published in | Journal of computational chemistry Vol. 28; no. 16; pp. 2552 - 2558 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.12.2007
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | http://dx.doi.org/10.1002/jcc.20750 istex:14F992DC29DB797D4BF99FBF93DFD322E70715A3 German national science foundation - No. DFG WE1863/10-2 ArticleID:JCC20750 Secretary of State for Science and Research through the Helmholtz-Society and the Kurt Eberhard Bode foundation ark:/67375/WNG-R8MCTNRC-0 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0192-8651 1096-987X |
DOI: | 10.1002/jcc.20750 |