Verma, Abhinav Gopal, Srinivasa M. Oh, Jung S. Lee, Kyu H. Wenzel, Wolfgang 2024-01-21T00:04:30Z 2024-01-21T00:04:30Z 2021-09-02 2007-12 0192-8651 https://pubs.kist.re.kr/handle/201004/133952 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 angstrom in < 24 h. (c) 2007 Wiley Periodicals, Inc. English WILEY REPLICA-EXCHANGE SIMULATIONS ENERGY LANDSCAPE STRUCTURE PREDICTION OPTIMIZATION MINIMIZATION PATHWAYS PEPTIDE SOLVATION COMPUTER MINIMUM All-atom de novo protein folding with a scalable evolutionary algorithm Article 10.1002/jcc.20750 1 JOURNAL OF COMPUTATIONAL CHEMISTRY, v.28, no.16, pp.2552 - 2558 JOURNAL OF COMPUTATIONAL CHEMISTRY 28 16 2552 2558 scie scopus 000250972500006 2-s2.0-35948929892 Chemistry, Multidisciplinary Chemistry Article REPLICA-EXCHANGE SIMULATIONS ENERGY LANDSCAPE STRUCTURE PREDICTION OPTIMIZATION MINIMIZATION PATHWAYS PEPTIDE SOLVATION COMPUTER MINIMUM De Novo protein folding evolutionary algorithm all-atom folding