(Q28307047)

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Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo

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Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo (English)

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Jinfeng Zhang

1

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S C Kou

series ordinal

2

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Jun S Liu

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3

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language of work or name

English

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publication date

14 June 2007

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published in

Journal of Chemical Physics

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volume

126

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issue

22

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page(s)

225101

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cites work

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The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk

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Kinetics of protein folding. A lattice model study of the requirements for folding to the native state.

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Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete

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A test of lattice protein folding algorithms

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Growth-based optimization algorithm for lattice heteropolymers.

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Growth algorithm for finding low energy configurations of simple lattice proteins

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A fast conformational search strategy for finding low energy structures of model proteins

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An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem

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A study of density of states and ground states in hydrophobic-hydrophilic protein folding models by equi-energy sampling.

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Contact interactions method: a new algorithm for protein folding simulations

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The Multiple-Try Method and Local Optimization in Metropolis Sampling

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Protein folding using contact maps.

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Optimization by Simulated Annealing

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Protein folding dynamics via quantification of kinematic energy landscape.

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Configurational bias Monte Carlo: a new sampling scheme for flexible chains

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Identifiers

DOI

10.1063/1.2736681

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Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4110084

OpenCitations bibliographic resource ID

4110084

1 reference

stated in

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

4110084

PubMed publication ID

17581081

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ResearchGate publication ID

6256359

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(Q28307047)

Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo

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