Literature DB >> 16392592

Foldamer dynamics expressed via Markov state models. I. Explicit solvent molecular-dynamics simulations in acetonitrile, chloroform, methanol, and water.

Sidney P Elmer1, Sanghyun Park, Vijay S Pande.   

Abstract

In this article, we analyze the folding dynamics of an all-atom model of a polyphenylacetylene (pPA) 12-mer in explicit solvent for four common organic and aqueous solvents: acetonitrile, chloroform, methanol, and water. The solvent quality has a dramatic effect on the time scales in which pPA 12-mers fold. Acetonitrile was found to manifest ideal folding conditions as suggested by optimal folding times on the order of approximately 100-200 ns, depending on temperature. In contrast, chloroform and water were observed to hinder the folding of the pPA 12-mer due to extreme solvation conditions relative to acetonitrile; chloroform denatures the oligomer, whereas water promotes aggregation and traps. The pPA 12-mer in a pure methanol solution folded in approximately 400 ns at 300 K, compared relative to the experimental 12-mer folding time of approximately 160 ns measured in a 1:1 v/v THF/methanol solution. Requisite in drawing the aforementioned conclusions, analysis techniques based on Markov state models are applied to multiple short independent trajectories to extrapolate the long-time scale dynamics of the 12-mer in each respective solvent. We review the theory of Markov chains and derive a method to impose detailed balance on a transition-probability matrix computed from simulation data.

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Year:  2005        PMID: 16392592     DOI: 10.1063/1.2001648

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  13 in total

Review 1.  Taming the complexity of protein folding.

Authors:  Gregory R Bowman; Vincent A Voelz; Vijay S Pande
Journal:  Curr Opin Struct Biol       Date:  2011-02       Impact factor: 6.809

2.  Using generalized ensemble simulations and Markov state models to identify conformational states.

Authors:  Gregory R Bowman; Xuhui Huang; Vijay S Pande
Journal:  Methods       Date:  2009-05-04       Impact factor: 3.608

3.  Building Markov state models along pathways to determine free energies and rates of transitions.

Authors:  Albert C Pan; Benoît Roux
Journal:  J Chem Phys       Date:  2008-08-14       Impact factor: 3.488

4.  Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint.

Authors:  Sergio Bacallado; John D Chodera; Vijay Pande
Journal:  J Chem Phys       Date:  2009-07-28       Impact factor: 3.488

Review 5.  The protein folding problem.

Authors:  Ken A Dill; S Banu Ozkan; M Scott Shell; Thomas R Weikl
Journal:  Annu Rev Biophys       Date:  2008       Impact factor: 12.981

6.  Threading a peptide through a peptide: protein loops, rotaxanes, and knots.

Authors:  John W Blankenship; Philip E Dawson
Journal:  Protein Sci       Date:  2007-06-13       Impact factor: 6.725

7.  Molecular dynamics simulation of drug uptake by polymer.

Authors:  M Subashini; Padma V Devarajan; Ganeshchandra S Sonavane; Mukesh Doble
Journal:  J Mol Model       Date:  2010-08-05       Impact factor: 1.810

Review 8.  Everything you wanted to know about Markov State Models but were afraid to ask.

Authors:  Vijay S Pande; Kyle Beauchamp; Gregory R Bowman
Journal:  Methods       Date:  2010-06-04       Impact factor: 3.608

9.  A microscopic view of phospholipid insertion into biological membranes.

Authors:  Josh V Vermaas; Emad Tajkhorshid
Journal:  J Phys Chem B       Date:  2013-12-16       Impact factor: 2.991

10.  Using Markov models to simulate electron spin resonance spectra from molecular dynamics trajectories.

Authors:  Deniz Sezer; Jack H Freed; Benoit Roux
Journal:  J Phys Chem B       Date:  2008-08-12       Impact factor: 2.991

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