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215 related items for PubMed ID: 15378533

  • 1. Biased fragment distribution in MC simulation of protein folding.
    Martineau E, L'Heureux PJ, Gunn JR.
    J Comput Chem; 2004 Nov 30; 25(15):1895-903. PubMed ID: 15378533
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  • 3. Monte Carlo vs molecular dynamics for all-atom polypeptide folding simulations.
    Ulmschneider JP, Ulmschneider MB, Di Nola A.
    J Phys Chem B; 2006 Aug 24; 110(33):16733-42. PubMed ID: 16913813
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  • 6. Study of the Villin headpiece folding dynamics by combining coarse-grained Monte Carlo evolution and all-atom molecular dynamics.
    De Mori GM, Colombo G, Micheletti C.
    Proteins; 2005 Feb 01; 58(2):459-71. PubMed ID: 15521059
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  • 7. The folding thermodynamics and kinetics of crambin using an all-atom Monte Carlo simulation.
    Shimada J, Kussell EL, Shakhnovich EI.
    J Mol Biol; 2001 Apr 20; 308(1):79-95. PubMed ID: 11302709
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  • 8. Folding simulations of small proteins.
    Kim SY, Lee J, Lee J.
    Biophys Chem; 2005 Apr 01; 115(2-3):195-200. PubMed ID: 15752604
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  • 9. Overcoming entropic barrier with coupled sampling at dual resolutions.
    Lwin TZ, Luo R.
    J Chem Phys; 2005 Nov 15; 123(19):194904. PubMed ID: 16321110
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  • 10. Assembly of protein structure from sparse experimental data: an efficient Monte Carlo model.
    Kolinski A, Skolnick J.
    Proteins; 1998 Sep 01; 32(4):475-94. PubMed ID: 9726417
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  • 11. New Monte Carlo algorithms for protein folding.
    Hansmann UH, Okamoto Y.
    Curr Opin Struct Biol; 1999 Apr 01; 9(2):177-83. PubMed ID: 10322208
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  • 12. Ab initio computational modeling of loops in G-protein-coupled receptors: lessons from the crystal structure of rhodopsin.
    Mehler EL, Hassan SA, Kortagere S, Weinstein H.
    Proteins; 2006 Aug 15; 64(3):673-90. PubMed ID: 16729264
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  • 13. Significance of conformational biases in Monte Carlo simulations of protein folding: lessons from Metropolis-Hastings approach.
    Przytycka T.
    Proteins; 2004 Nov 01; 57(2):338-44. PubMed ID: 15340921
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  • 14. Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations.
    Okamoto Y.
    J Mol Graph Model; 2004 May 01; 22(5):425-39. PubMed ID: 15099838
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  • 15. A new Hybrid Monte Carlo algorithm for protein potential function test and structure refinement.
    Zhang H.
    Proteins; 1999 Mar 01; 34(4):464-71. PubMed ID: 10081959
    [Abstract] [Full Text] [Related]

  • 16. Genetic algorithms for protein folding simulations.
    Unger R, Moult J.
    J Mol Biol; 1993 May 05; 231(1):75-81. PubMed ID: 8496967
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  • 17. A knowledge-based move set for protein folding.
    Chen WW, Yang JS, Shakhnovich EI.
    Proteins; 2007 Feb 15; 66(3):682-8. PubMed ID: 17143895
    [Abstract] [Full Text] [Related]

  • 18. Towards realistic description of collective motions in the lattice protein folding models.
    Yesylevskyy SO, Demchenko AP.
    Biophys Chem; 2004 Apr 01; 109(1):17-40. PubMed ID: 15059657
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  • 19. Fast protein structure prediction using Monte Carlo simulations with modal moves.
    Carnevali P, Tóth G, Toubassi G, Meshkat SN.
    J Am Chem Soc; 2003 Nov 26; 125(47):14244-5. PubMed ID: 14624550
    [Abstract] [Full Text] [Related]

  • 20. Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide.
    Totrov M, Abagyan R.
    Biopolymers; 2001 Nov 26; 60(2):124-33. PubMed ID: 11455546
    [Abstract] [Full Text] [Related]

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