Literature DB >> 24027492

Quantifying hub-like behavior in protein folding networks.

Alex Dickson1, Charles L Brooks.   

Abstract

The free energy landscape of a protein is a function of many interdependent degrees of freedom. For this reason, conceptual constructs (e.g., funnels) have been useful to visualize these landscapes. One relatively new construct is the idea of a hub-like native state that is the final destination of many non-interconverting folding pathways. This is in contrast to the idea of a single predominant folding pathway connecting the native state to a rapidly interconverting ensemble of unfolded states. The key quantity to distinguish between these two ideas is the connectivity of the unfolded ensemble. We present a metric to determine this connectivity for a given network, which can be calculated either from continuous folding trajectories, or a Markov model. The metric determines how often a region of space is used as an intermediate on transition paths that connect two other regions of space, and we use it here to determine how often two parts of the unfolded ensemble are connected directly, versus how often these transitions are mediated by the native state.

Entities:  

Year:  2012        PMID: 24027492      PMCID: PMC3767461          DOI: 10.1021/ct300537s

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  24 in total

Review 1.  Protein folding and misfolding.

Authors:  Christopher M Dobson
Journal:  Nature       Date:  2003-12-18       Impact factor: 49.962

2.  The protein folding network.

Authors:  Francesco Rao; Amedeo Caflisch
Journal:  J Mol Biol       Date:  2004-09-03       Impact factor: 5.469

3.  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

4.  Separating forward and backward pathways in nonequilibrium umbrella sampling.

Authors:  Alex Dickson; Aryeh Warmflash; Aaron R Dinner
Journal:  J Chem Phys       Date:  2009-10-21       Impact factor: 3.488

Review 5.  CHARMM: the biomolecular simulation program.

Authors:  B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus
Journal:  J Comput Chem       Date:  2009-07-30       Impact factor: 3.376

6.  Folding processes of the B domain of protein A to the native state observed in all-atom ab initio folding simulations.

Authors:  Hongxing Lei; Chun Wu; Zhi-Xiang Wang; Yaoqi Zhou; Yong Duan
Journal:  J Chem Phys       Date:  2008-06-21       Impact factor: 3.488

7.  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

8.  Simple theory of protein folding kinetics.

Authors:  Vijay S Pande
Journal:  Phys Rev Lett       Date:  2010-11-05       Impact factor: 9.161

9.  First-principles calculation of the folding free energy of a three-helix bundle protein.

Authors:  E M Boczko; C L Brooks
Journal:  Science       Date:  1995-07-21       Impact factor: 47.728

Review 10.  Folding proteins in fatal ways.

Authors:  Dennis J Selkoe
Journal:  Nature       Date:  2003-12-18       Impact factor: 49.962

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  17 in total

1.  First Passage Times, Lifetimes, and Relaxation Times of Unfolded Proteins.

Authors:  Wei Dai; Anirvan M Sengupta; Ronald M Levy
Journal:  Phys Rev Lett       Date:  2015-07-21       Impact factor: 9.161

2.  How long does it take to equilibrate the unfolded state of a protein?

Authors:  Ronald M Levy; Wei Dai; Nan-Jie Deng; Dmitrii E Makarov
Journal:  Protein Sci       Date:  2013-09-17       Impact factor: 6.725

3.  Probing the origins of two-state folding.

Authors:  Thomas J Lane; Christian R Schwantes; Kyle A Beauchamp; Vijay S Pande
Journal:  J Chem Phys       Date:  2013-10-14       Impact factor: 3.488

4.  Markov state models of protein misfolding.

Authors:  Anshul Sirur; David De Sancho; Robert B Best
Journal:  J Chem Phys       Date:  2016-02-21       Impact factor: 3.488

5.  Efficient in silico exploration of RNA interhelical conformations using Euler angles and WExplore.

Authors:  Alex Dickson; Anthony M Mustoe; Loïc Salmon; Charles L Brooks
Journal:  Nucleic Acids Res       Date:  2014-10-07       Impact factor: 16.971

6.  Network visualization of conformational sampling during molecular dynamics simulation.

Authors:  Logan S Ahlstrom; Joseph Lee Baker; Kent Ehrlich; Zachary T Campbell; Sunita Patel; Ivan I Vorontsov; Florence Tama; Osamu Miyashita
Journal:  J Mol Graph Model       Date:  2013-10-16       Impact factor: 2.518

7.  Native states of fast-folding proteins are kinetic traps.

Authors:  Alex Dickson; Charles L Brooks
Journal:  J Am Chem Soc       Date:  2013-03-15       Impact factor: 15.419

8.  How kinetics within the unfolded state affects protein folding: an analysis based on markov state models and an ultra-long MD trajectory.

Authors:  Nan-jie Deng; Wei Dai; Ronald M Levy
Journal:  J Phys Chem B       Date:  2013-06-13       Impact factor: 2.991

9.  WExplore: hierarchical exploration of high-dimensional spaces using the weighted ensemble algorithm.

Authors:  Alex Dickson; Charles L Brooks
Journal:  J Phys Chem B       Date:  2014-02-11       Impact factor: 2.991

10.  Approximating frustration scores in complex networks via perturbed Laplacian spectra.

Authors:  Andrej J Savol; Chakra S Chennubhotla
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-12-04
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