Literature DB >> 19887634

Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations.

Frank Noé1, Christof Schütte, Eric Vanden-Eijnden, Lothar Reich, Thomas R Weikl.   

Abstract

Characterizing the equilibrium ensemble of folding pathways, including their relative probability, is one of the major challenges in protein folding theory today. Although this information is in principle accessible via all-atom molecular dynamics simulations, it is difficult to compute in practice because protein folding is a rare event and the affordable simulation length is typically not sufficient to observe an appreciable number of folding events, unless very simplified protein models are used. Here we present an approach that allows for the reconstruction of the full ensemble of folding pathways from simulations that are much shorter than the folding time. This approach can be applied to all-atom protein simulations in explicit solvent. It does not use a predefined reaction coordinate but is based on partitioning the state space into small conformational states and constructing a Markov model between them. A theory is presented that allows for the extraction of the full ensemble of transition pathways from the unfolded to the folded configurations. The approach is applied to the folding of a PinWW domain in explicit solvent where the folding time is two orders of magnitude larger than the length of individual simulations. The results are in good agreement with kinetic experimental data and give detailed insights about the nature of the folding process which is shown to be surprisingly complex and parallel. The analysis reveals the existence of misfolded trap states outside the network of efficient folding intermediates that significantly reduce the folding speed.

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Year:  2009        PMID: 19887634      PMCID: PMC2772816          DOI: 10.1073/pnas.0905466106

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  31 in total

1.  On the simulation of protein folding by short time scale molecular dynamics and distributed computing.

Authors:  Alan R Fersht
Journal:  Proc Natl Acad Sci U S A       Date:  2002-10-18       Impact factor: 11.205

2.  A protein folding pathway with multiple folding intermediates at atomic resolution.

Authors:  Hanqiao Feng; Zheng Zhou; Yawen Bai
Journal:  Proc Natl Acad Sci U S A       Date:  2005-03-25       Impact factor: 11.205

Review 3.  Malleability of protein folding pathways: a simple reason for complex behaviour.

Authors:  Magnus O Lindberg; Mikael Oliveberg
Journal:  Curr Opin Struct Biol       Date:  2007-01-23       Impact factor: 6.809

4.  Transition states in protein folding kinetics: modeling phi-values of small beta-sheet proteins.

Authors:  Thomas R Weikl
Journal:  Biophys J       Date:  2007-09-28       Impact factor: 4.033

5.  Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece.

Authors:  Daniel L Ensign; Peter M Kasson; Vijay S Pande
Journal:  J Mol Biol       Date:  2007-09-29       Impact factor: 5.469

6.  Ten-microsecond molecular dynamics simulation of a fast-folding WW domain.

Authors:  Peter L Freddolino; Feng Liu; Martin Gruebele; Klaus Schulten
Journal:  Biophys J       Date:  2008-03-13       Impact factor: 4.033

Review 7.  Combining experiment and simulation in protein folding: closing the gap for small model systems.

Authors:  R Dustin Schaeffer; Alan Fersht; Valerie Daggett
Journal:  Curr Opin Struct Biol       Date:  2008-02-01       Impact factor: 6.809

8.  Coarse master equations for peptide folding dynamics.

Authors:  Nicolae-Viorel Buchete; Gerhard Hummer
Journal:  J Phys Chem B       Date:  2008-01-31       Impact factor: 2.991

9.  Probability distributions of molecular observables computed from Markov models.

Authors:  Frank Noé
Journal:  J Chem Phys       Date:  2008-06-28       Impact factor: 3.488

Review 10.  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

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  227 in total

1.  Dominant folding pathways of a WW domain.

Authors:  Silvio A Beccara; Tatjana Škrbić; Roberto Covino; Pietro Faccioli
Journal:  Proc Natl Acad Sci U S A       Date:  2012-01-26       Impact factor: 11.205

2.  Simple few-state models reveal hidden complexity in protein folding.

Authors:  Kyle A Beauchamp; Robert McGibbon; Yu-Shan Lin; Vijay S Pande
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-09       Impact factor: 11.205

3.  Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites.

Authors:  Gregory R Bowman; Phillip L Geissler
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-02       Impact factor: 11.205

4.  Energy landscape and multiroute folding of topologically complex proteins adenylate kinase and 2ouf-knot.

Authors:  Wenfei Li; Tsuyoshi Terakawa; Wei Wang; Shoji Takada
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-02       Impact factor: 11.205

5.  Protein dynamics investigated by inherent structure analysis.

Authors:  Francesco Rao; Martin Karplus
Journal:  Proc Natl Acad Sci U S A       Date:  2010-04-30       Impact factor: 11.205

6.  Protein folded states are kinetic hubs.

Authors:  Gregory R Bowman; Vijay S Pande
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-01       Impact factor: 11.205

Review 7.  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

8.  Atomistic folding simulations of the five-helix bundle protein λ(6−85).

Authors:  Gregory R Bowman; Vincent A Voelz; Vijay S Pande
Journal:  J Am Chem Soc       Date:  2011-02-02       Impact factor: 15.419

9.  Dynamics of an ultrafast folding subdomain in the context of a larger protein fold.

Authors:  Caitlin M Davis; R Brian Dyer
Journal:  J Am Chem Soc       Date:  2013-12-13       Impact factor: 15.419

10.  Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning.

Authors:  Yasuhiro Matsunaga; Yuji Sugita
Journal:  Elife       Date:  2018-05-03       Impact factor: 8.140

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