Literature DB >> 21368203

Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments.

Frank Noé1, Sören Doose, Isabella Daidone, Marc Löllmann, Markus Sauer, John D Chodera, Jeremy C Smith.   

Abstract

There is a gap between kinetic experiment and simulation in their views of the dynamics of complex biomolecular systems. Whereas experiments typically reveal only a few readily discernible exponential relaxations, simulations often indicate complex multistate behavior. Here, a theoretical framework is presented that reconciles these two approaches. The central concept is "dynamical fingerprints" which contain peaks at the time scales of the dynamical processes involved with amplitudes determined by the experimental observable. Fingerprints can be generated from both experimental and simulation data, and their comparison by matching peaks permits assignment of structural changes present in the simulation to experimentally observed relaxation processes. The approach is applied here to a test case interpreting single molecule fluorescence correlation spectroscopy experiments on a set of fluorescent peptides with molecular dynamics simulations. The peptides exhibit complex kinetics shown to be consistent with the apparent simplicity of the experimental data. Moreover, the fingerprint approach can be used to design new experiments with site-specific labels that optimally probe specific dynamical processes in the molecule under investigation.

Mesh:

Year:  2011        PMID: 21368203      PMCID: PMC3064371          DOI: 10.1073/pnas.1004646108

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


  33 in total

1.  On the relationship among three theories of relaxation in disordered systems.

Authors:  J Klafter; M F Shlesinger
Journal:  Proc Natl Acad Sci U S A       Date:  1986-02       Impact factor: 11.205

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Authors:  Nicolae-Viorel Buchete; Gerhard Hummer
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Authors:  H Frauenfelder; S G Sligar; P G Wolynes
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5.  A unified model of protein dynamics.

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Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-27       Impact factor: 11.205

6.  Dynamics of unfolded polypeptide chains as model for the earliest steps in protein folding.

Authors:  Florian Krieger; Beat Fierz; Oliver Bieri; Mario Drewello; Thomas Kiefhaber
Journal:  J Mol Biol       Date:  2003-09-05       Impact factor: 5.469

7.  A fluorescence-based method for direct measurement of submicrosecond intramolecular contact formation in biopolymers: an exploratory study with polypeptides.

Authors:  Robert R Hudgins; Fang Huang; Gabriela Gramlich; Werner M Nau
Journal:  J Am Chem Soc       Date:  2002-01-30       Impact factor: 15.419

8.  Progress and challenges in the automated construction of Markov state models for full protein systems.

Authors:  Gregory R Bowman; Kyle A Beauchamp; George Boxer; Vijay S Pande
Journal:  J Chem Phys       Date:  2009-09-28       Impact factor: 3.488

9.  Kinetic analysis of molecular dynamics simulations reveals changes in the denatured state and switch of folding pathways upon single-point mutation of a beta-sheet miniprotein.

Authors:  Stefanie Muff; Amedeo Caflisch
Journal:  Proteins       Date:  2008-03

10.  Submillisecond protein folding kinetics studied by ultrarapid mixing.

Authors:  C K Chan; Y Hu; S Takahashi; D L Rousseau; W A Eaton; J Hofrichter
Journal:  Proc Natl Acad Sci U S A       Date:  1997-03-04       Impact factor: 11.205

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  30 in total

1.  Ultrafast energy flow in the wake of solution-phase bimolecular reactions.

Authors:  David R Glowacki; Rebecca A Rose; Stuart J Greaves; Andrew J Orr-Ewing; Jeremy N Harvey
Journal:  Nat Chem       Date:  2011-09-25       Impact factor: 24.427

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.  Dynamic graphical models of molecular kinetics.

Authors:  Simon Olsson; Frank Noé
Journal:  Proc Natl Acad Sci U S A       Date:  2019-07-08       Impact factor: 11.205

4.  The dynamic conformational landscape of the protein methyltransferase SETD8.

Authors:  Shi Chen; Rafal P Wiewiora; Fanwang Meng; Nicolas Babault; Anqi Ma; Wenyu Yu; Kun Qian; Hao Hu; Hua Zou; Junyi Wang; Shijie Fan; Gil Blum; Fabio Pittella-Silva; Kyle A Beauchamp; Wolfram Tempel; Hualiang Jiang; Kaixian Chen; Robert J Skene; Yujun George Zheng; Peter J Brown; Jian Jin; Cheng Luo; John D Chodera; Minkui Luo
Journal:  Elife       Date:  2019-05-13       Impact factor: 8.140

5.  Identification of kinetic order parameters for non-equilibrium dynamics.

Authors:  Fabian Paul; Hao Wu; Maximilian Vossel; Bert L de Groot; Frank Noé
Journal:  J Chem Phys       Date:  2019-04-28       Impact factor: 3.488

6.  Combining experimental and simulation data of molecular processes via augmented Markov models.

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Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-17       Impact factor: 11.205

7.  Enspara: Modeling molecular ensembles with scalable data structures and parallel computing.

Authors:  J R Porter; M I Zimmerman; G R Bowman
Journal:  J Chem Phys       Date:  2019-01-28       Impact factor: 3.488

Review 8.  To milliseconds and beyond: challenges in the simulation of protein folding.

Authors:  Thomas J Lane; Diwakar Shukla; Kyle A Beauchamp; Vijay S Pande
Journal:  Curr Opin Struct Biol       Date:  2012-12-10       Impact factor: 6.809

9.  Slow unfolded-state structuring in Acyl-CoA binding protein folding revealed by simulation and experiment.

Authors:  Vincent A Voelz; Marcus Jäger; Shuhuai Yao; Yujie Chen; Li Zhu; Steven A Waldauer; Gregory R Bowman; Mark Friedrichs; Olgica Bakajin; Lisa J Lapidus; Shimon Weiss; Vijay S Pande
Journal:  J Am Chem Soc       Date:  2012-07-19       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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