All-atom de novo protein folding with a scalable evolutionary algorithm

• Published in: Journal of computational chemistry Vol. 28; no. 16; pp. 2552 - 2558
• Main Authors: Verma, Abhinav, Gopal, Srinivasa M, Oh, Jung S, Lee, Kyu H, Wenzel, Wolfgang
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2007; Wiley Subscription Services, Inc
• Subjects: 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
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.20750

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