Comparing search strategies for finding global optima on energy landscapes

Foreman, K. W.; Phillips, A. T.; Rosen, Jessica; Dill, Ken A.

doi:10.1002/(sici)1096-987x(19991115)20:14<1527::aid-jcc5>3.3.co;2-n

Cited by 6 publications

(8 citation statements)

References 2 publications

(2 reference statements)

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“…Cootes et al found that such methods speeded the Monte Carlo search, provided more accurate estimates of global optima for known test cases, but were not as fast as mean-field methods. Foreman et al developed a new global optimization method which works well for rugged energy surfaces with an overall funnel topology . Such methods may eventually prove useful for protein design.…”

Section: Search Methodsmentioning

confidence: 99%

Designing Protein Energy Landscapes

Saven

2001

Chem. Rev.

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Section: Search Methodsmentioning

confidence: 99%

Designing Protein Energy Landscapes

Saven

2001

Chem. Rev.

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“…The search space contains a large number of equivalent solutions because there are N ! ways to order the N atoms in any local minima [8]. The 38 atoms test problem is particularly interesting because it has been shown to be more difficult than larger clusters because it has two very competitive solutions that have a distinctly different atomic structure [6] resulting in two distinct clusters of local optima.…”

Section: Related Workmentioning

confidence: 99%

The dispersion metric and the CMA evolution strategy

Lunacek

Whitley

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

130

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An algorithm independent metric is introduced that measures the dispersion of a uniform random sample drawn from the top ranked percentiles of the search space. A low dispersion function is one where the dispersion decreases as the sample is restricted to better regions of the search space. A high dispersion function is one where dispersion stay constant or increases as the sample is restricted to better regions of the search space. This distinction can be used to explain why the CMA Evolution Strategy is more efficient on some multimodal problems than on others.

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“…First, knowledge of landscapes will be of benefit in designing faster and more robust computer methods for predicting native structures (6,7). Second, a goal of computational biology is not just to predict native structures, per se, but to predict function.…”

mentioning

confidence: 99%

RNA folding energy landscapes

Chen

Dill²

2000

Proc. Natl. Acad. Sci. U.S.A.

197

147

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Using a statistical mechanical treatment, we study RNA folding energy landscapes. We first validate the theory by showing that, for the RNA molecules we tested having only secondary structures, this treatment (i) predicts about the same native structures as the Zuker method, and (ii) qualitatively predicts the melting curve peaks and shoulders seen in experiments. We then predict thermodynamic folding intermediates. For one hairpin sequence, unfolding is a simple unzipping process. But for another sequence, unfolding is more complex. It involves multiple stable intermediates and a rezipping into a completely non-native conformation before unfolding. The principle that emerges, for which there is growing experimental support, is that although protein folding tends to involve highly cooperative two-state thermodynamic transitions, without detectable intermediates, the folding of RNA secondary structures may involve rugged landscapes, often with more complex intermediate states.A t the center of computational biology is the folding problem for proteins or RNA molecules: to predict the conformation having the global minimum energy from the monomer sequence. This problem is not yet solved. But even when it is solved, it will only give us a small fraction of the information we would like to have about biomolecule folding. We would also like to know how the folding process takes place, what are the folding routes, the folding thermodynamics and cooperativity, intermediate states, transition states, and conformational transitions. To understand these properties requires knowledge of more than just the single native conformation. It requires the full energy landscape (1-5): the free energies of all of the chain conformations as a function of the microscopic degrees of freedom of the molecule.There are two practical reasons that it is important to know energy landscapes. First, knowledge of landscapes will be of benefit in designing faster and more robust computer methods for predicting native structures (6, 7). Second, a goal of computational biology is not just to predict native structures, per se, but to predict function. Ligand binding to proteins and RNA molecules, and catalytic mechanisms, are often more dependent on the conformations that are fluctuations away from the native structure than on the native conformation itself (8). To predict the fluctuations, we need energy landscapes.Recent theoretical and experimental advances are beginning to go beyond native structures to shed light on full RNA folding energy landscapes (9-19). But so far, such landscapes have not yet been predictable from monomer sequences. There is one class of biomolecule conformations-RNA secondary structures-for which folding algorithms are fairly successful. A popular method for predicting the native secondary structures of RNA molecules has been developed by . In this paper, we describe a method for going beyond the prediction of such single points on landscapes. Our method predicts the full energy landscape for RNA secondary structures as...

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Comparing search strategies for finding global optima on energy landscapes

Cited by 6 publications

References 2 publications

Designing Protein Energy Landscapes

Designing Protein Energy Landscapes

The dispersion metric and the CMA evolution strategy

RNA folding energy landscapes

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