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 |
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01.12.2007
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| Abstract | 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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| AbstractList | Abstract
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 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 ... in <24 h. (ProQuest: ... denotes formulae/symbols omitted.) 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. 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 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. |
| Author | Verma, Abhinav Oh, Jung S. Wenzel, Wolfgang Gopal, Srinivasa M. Lee, Kyu H. |
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| Snippet | The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational... The search for efficient and predictive methods to describe the protein folding process at the all‐atom level remains an important grand‐computational... Abstract The search for efficient and predictive methods to describe the protein folding process at the all‐atom level remains an important grand‐computational... |
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| SubjectTerms | Algorithms all-atom folding Amino acids Atoms & subatomic particles Computer Simulation De Novo protein folding evolutionary algorithm Human Immunodeficiency Virus Proteins - chemistry Humans Models, Molecular Optimization algorithms Protein Conformation Protein Folding |
| Title | All-atom de novo protein folding with a scalable evolutionary algorithm |
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