title: Folding small proteins via annealing stochastic approximation Monte Carlo.
abstract: Recently, the stochastic approximation Monte Carlo algorithm has been proposed by Liang et al. (2007) as a general-purpose stochastic optimization and simulation algorithm. An annealing version of this algorithm was developed for real small protein folding problems. The numerical results indicate that it outperforms simulated annealing and conventional Monte Carlo algorithms as a stochastic optimization algorithm. We also propose one method for the use of secondary structures in protein folding. The predicted protein structures are rather close to the true structures.
authors.affiliation: Department of Informational Statistics, Korea University, Jochiwon, South Korea. scheon@korea.ac.kr
authors.forename: Sooyoung
authors.initials: S
authors.lastname: Cheon
authors.forename: Faming
authors.initials: F
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008954:Models, Biological; D009010:Monte Carlo Method; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D013269:Stochastic Processes
chemical_list: D011506:Proteins