Literature DB >> 22385857

Protein folding is mechanistically robust.

Jeffrey K Weber1, Vijay S Pande.   

Abstract

Markov state models (MSMs) have proven to be useful tools in simulating large and slowly-relaxing biological systems like proteins. MSMs model proteins through dynamics on a discrete-state energy landscape, allowing molecules to effectively sample large regions of phase space. In this work, we use aspects of MSMs to ask: is protein folding mechanistically robust? We first provide a definition of mechanism in the context of Markovian models, and we later use perturbation theory and the concept of parametric sloppiness to show that parts of the MSM eigenspectrum are resistant to perturbation. We introduce a new, to our knowledge, Bayesian metric by which eigenspectrum robustness can be evaluated, and we discuss the implications of mechanistic robustness and possible new applications of MSMs to understanding biophysical phenomena. Copyright Â
© 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Mesh:

Year:  2012        PMID: 22385857      PMCID: PMC3283819          DOI: 10.1016/j.bpj.2012.01.028

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  23 in total

1.  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

2.  Sloppy-model universality class and the Vandermonde matrix.

Authors:  Joshua J Waterfall; Fergal P Casey; Ryan N Gutenkunst; Kevin S Brown; Christopher R Myers; Piet W Brouwer; Veit Elser; James P Sethna
Journal:  Phys Rev Lett       Date:  2006-10-12       Impact factor: 9.161

3.  Reactive flux and folding pathways in network models of coarse-grained protein dynamics.

Authors:  Alexander Berezhkovskii; Gerhard Hummer; Attila Szabo
Journal:  J Chem Phys       Date:  2009-05-28       Impact factor: 3.488

4.  Mapping the conformational transition in Src activation by cumulating the information from multiple molecular dynamics trajectories.

Authors:  Sichun Yang; Nilesh K Banavali; Benoît Roux
Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-18       Impact factor: 11.205

Review 5.  Sloppiness, robustness, and evolvability in systems biology.

Authors:  Bryan C Daniels; Yan-Jiun Chen; James P Sethna; Ryan N Gutenkunst; Christopher R Myers
Journal:  Curr Opin Biotechnol       Date:  2008-07-25       Impact factor: 9.740

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

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

7.  Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39).

Authors:  Vincent A Voelz; Gregory R Bowman; Kyle Beauchamp; Vijay S Pande
Journal:  J Am Chem Soc       Date:  2010-02-10       Impact factor: 15.419

8.  Rapid equilibrium sampling initiated from nonequilibrium data.

Authors:  Xuhui Huang; Gregory R Bowman; Sergio Bacallado; Vijay S Pande
Journal:  Proc Natl Acad Sci U S A       Date:  2009-09-29       Impact factor: 11.205

Review 9.  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

10.  Universally sloppy parameter sensitivities in systems biology models.

Authors:  Ryan N Gutenkunst; Joshua J Waterfall; Fergal P Casey; Kevin S Brown; Christopher R Myers; James P Sethna
Journal:  PLoS Comput Biol       Date:  2007-08-15       Impact factor: 4.475

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  5 in total

1.  Elucidation of the Aggregation Pathways of Helix-Turn-Helix Peptides: Stabilization at the Turn Region Is Critical for Fibril Formation.

Authors:  Thanh D Do; Ali Chamas; Xueyun Zheng; Aaron Barnes; Dayna Chang; Tjitske Veldstra; Harmeet Takhar; Nicolette Dressler; Benjamin Trapp; Kylie Miller; Audrene McMahon; Stephen C Meredith; Joan-Emma Shea; Kristi Lazar Cantrell; Michael T Bowers
Journal:  Biochemistry       Date:  2015-06-24       Impact factor: 3.162

2.  A simple model predicts experimental folding rates and a hub-like topology.

Authors:  Thomas J Lane; Vijay S Pande
Journal:  J Phys Chem B       Date:  2012-04-11       Impact factor: 2.991

Review 3.  Markov state models of biomolecular conformational dynamics.

Authors:  John D Chodera; Frank Noé
Journal:  Curr Opin Struct Biol       Date:  2014-05-16       Impact factor: 6.809

4.  Quantifying the Sources of Kinetic Frustration in Folding Simulations of Small Proteins.

Authors:  Andrej J Savol; Chakra S Chennubhotla
Journal:  J Chem Theory Comput       Date:  2014-06-13       Impact factor: 6.006

5.  Identifiability, reducibility, and adaptability in allosteric macromolecules.

Authors:  Gergő Bohner; Gaurav Venkataraman
Journal:  J Gen Physiol       Date:  2017-04-17       Impact factor: 4.086

  5 in total

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