Stochastic optimization methods prediction

Summary. We recently developed an all-atom free energy force field (PFFOl) for protein structure prediction with stochastic optimization methods. We demonstrated that PFFOl correctly predicts the native conformation of several proteins as the global optimum of the free energy surface. Here we review recent folding studies, which permitted the reproducible all-atom folding of the 20 amino-acid trp-cage protein, the 40-amino acid three-helix HIV accessory protein and the sixty amino acid bacterial ribosomal protein L20 with a variety of stochastic optimization methods. These results demonstrate that all-atom protein folding can be achieved with present day computational resources for proteins of moderate size. 

This review indicates that all-atom protein structure prediction with stochastic optimization methods becomes feasible with present-day computational resources. The fact that three proteins were reproducibly folded with different optimization methods to near-native conformation increases the confidence in the parameterization of our all-atom protein force field PFFOl. The... 

To identify potentially active compounds in the virtual library, FOCUS-2D employs stochastic optimization methods such as SA (228, 229) and (jA (230-232). The latter algorithm was used for targeted pentapeptide library design as follows. Initially, a population of 100 peptides is randomly generated and encoded by use of topological indices or amino acid-dependent physicochemical descriptors. The fitness of each peptide is evaluated by its biological activity predicted from a precon-structed QSAR equation (see below). Two par-... 

Fitting model predictions to experimental observations can be performed in the Laplace, Fourier or time domains with optimal parameter choices often being made using weighted residuals techniques. James et al. [71] review and compare least squares, stochastic and hill-climbing methods for evaluating parameters and Froment and Bischoff [16] summarise some of the more common methods and warn that ordinary moments matching-techniques appear to be less reliable than alternative procedures. References 72 and 73 are studies of the errors associated with a selection of parameter extraction routines. 

In contrast to optimal design, Hamprecht and co-workers recently introdnced a space-filling design techniqne for compound selection. This stochastic method nses the best linear unbiased estimator, in the form of Kriging, " to constrnct selection designs that optimize the integrated mean-square prediction error, or entropy. This... 

This chapter provides an overview of the most frequently applied numerical methods for the simulation of polymerization processes, that is, die calculation of the polymer microstructure as a function of monomer conversion and process conditions such as the temperature and initial concentrations. It is important to note that such simulations allow one to optimize the macroscopic polymer properties and to influence the polymer processability and final polymer product application range. Both deterministic and stochastic modeling techniques are discussed. In deterministic modeling techniques, time variation is seen as a continuous and predictable process, whereas in stochastic modeling techniques, a random-walk process is assumed instead. 

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