Literature DB >> 19466817

Revisiting the finite temperature string method for the calculation of reaction tubes and free energies.

Eric Vanden-Eijnden1, Maddalena Venturoli.   

Abstract

An improved and simplified version of the finite temperature string (FTS) method [W. E, W. Ren, and E. Vanden-Eijnden, J. Phys. Chem. B 109, 6688 (2005)] is proposed. Like the original approach, the new method is a scheme to calculate the principal curves associated with the Boltzmann-Gibbs probability distribution of the system, i.e., the curves which are such that their intersection with the hyperplanes perpendicular to themselves coincides with the expected position of the system in these planes (where perpendicular is understood with respect to the appropriate metric). Unlike more standard paths such as the minimum energy path or the minimum free energy path, the location of the principal curve depends on global features of the energy or the free energy landscapes and thereby may remain appropriate in situations where the landscape is rough on the thermal energy scale and/or entropic effects related to the width of the reaction channels matter. Instead of using constrained sampling in hyperplanes as in the original FTS, the new method calculates the principal curve via sampling in the Voronoi tessellation whose generating points are the discretization points along this curve. As shown here, this modification results in greater algorithmic simplicity. As a by-product, it also gives the free energy associated with the Voronoi tessellation. The new method can be applied both in the original Cartesian space of the system or in a set of collective variables. We illustrate FTS on test-case examples and apply it to the study of conformational transitions of the nitrogen regulatory protein C receiver domain using an elastic network model and to the isomerization of solvated alanine dipeptide.

Entities:  

Year:  2009        PMID: 19466817     DOI: 10.1063/1.3130083

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


  46 in total

1.  "DFG-flip" in the insulin receptor kinase is facilitated by a helical intermediate state of the activation loop.

Authors:  Harish Vashisth; Luca Maragliano; Cameron F Abrams
Journal:  Biophys J       Date:  2012-04-18       Impact factor: 4.033

2.  DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks.

Authors:  Margaret J Tse; Brian K Chu; Mahua Roy; Elizabeth L Read
Journal:  Biophys J       Date:  2015-10-20       Impact factor: 4.033

3.  Maximum Flux Transition Paths of Conformational Change.

Authors:  Ruijun Zhao; Juanfang Shen; Robert D Skeel
Journal:  J Chem Theory Comput       Date:  2010-08-10       Impact factor: 6.006

4.  Catch bond-like kinetics of helix cracking: network analysis by molecular dynamics and milestoning.

Authors:  Steven M Kreuzer; Tess J Moon; Ron Elber
Journal:  J Chem Phys       Date:  2013-09-28       Impact factor: 3.488

5.  Unsuspected pathway of the allosteric transition in hemoglobin.

Authors:  Stefan Fischer; Kenneth W Olsen; Kwangho Nam; Martin Karplus
Journal:  Proc Natl Acad Sci U S A       Date:  2011-03-17       Impact factor: 11.205

6.  Free energy of conformational transition paths in biomolecules: the string method and its application to myosin VI.

Authors:  Victor Ovchinnikov; Martin Karplus; Eric Vanden-Eijnden
Journal:  J Chem Phys       Date:  2011-02-28       Impact factor: 3.488

7.  Simulating rare events using a weighted ensemble-based string method.

Authors:  Joshua L Adelman; Michael Grabe
Journal:  J Chem Phys       Date:  2013-01-28       Impact factor: 3.488

8.  Unrestrained computation of free energy along a path.

Authors:  Bradley M Dickson; He Huang; Carol Beth Post
Journal:  J Phys Chem B       Date:  2012-08-30       Impact factor: 2.991

9.  Investigations of α-helix↔β-sheet transition pathways in a miniprotein using the finite-temperature string method.

Authors:  Victor Ovchinnikov; Martin Karplus
Journal:  J Chem Phys       Date:  2014-05-07       Impact factor: 3.488

10.  On the assumptions underlying milestoning.

Authors:  Eric Vanden-Eijnden; Maddalena Venturoli; Giovanni Ciccotti; Ron Elber
Journal:  J Chem Phys       Date:  2008-11-07       Impact factor: 3.488

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