Literature DB >> 23408514

Correlation Analysis of Enzymatic Reaction of a Single Protein Molecule.

Chao Du1, S C Kou.   

Abstract

New advances in nano sciences open the door for scientists to study biological processes on a microscopic molecule-by-molecule basis. Recent single-molecule biophysical experiments on enzyme systems, in particular, reveal that enzyme molecules behave fundamentally differently from what classical model predicts. A stochastic network model was previously proposed to explain the experimental discovery. This paper conducts detailed theoretical and data analyses of the stochastic network model, focusing on the correlation structure of the successive reaction times of a single enzyme molecule. We investigate the correlation of experimental fluorescence intensity and the correlation of enzymatic reaction times, and examine the role of substrate concentration in enzymatic reactions. Our study shows that the stochastic network model is capable of explaining the experimental data in depth.

Entities:  

Keywords:  Autocorrelation; Michaelis-Menten model; continuous time Markov chain; fluorescence intensity; single-molecule experiment; stochastic network model; turnover time

Year:  2012        PMID: 23408514      PMCID: PMC3568780          DOI: 10.1214/12-AOAS541

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN: 1932-6157            Impact factor:   2.083


  17 in total

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Authors:  X S Xie; H P Lu
Journal:  J Biol Chem       Date:  1999-06-04       Impact factor: 5.157

2.  Kinesin moves by an asymmetric hand-over-hand mechanism.

Authors:  Charles L Asbury; Adrian N Fehr; Steven M Block
Journal:  Science       Date:  2003-12-04       Impact factor: 47.728

3.  Protein conformational dynamics probed by single-molecule electron transfer.

Authors:  Haw Yang; Guobin Luo; Pallop Karnchanaphanurach; Tai-Man Louie; Ivan Rech; Sergio Cova; Luying Xun; X Sunney Xie
Journal:  Science       Date:  2003-10-10       Impact factor: 47.728

4.  Stretched exponential decay and correlations in the catalytic activity of fluctuating single lipase molecules.

Authors:  Ophir Flomenbom; Kelly Velonia; Davey Loos; Sadahiro Masuo; Mircea Cotlet; Yves Engelborghs; Johan Hofkens; Alan E Rowan; Roeland J M Nolte; Mark Van der Auweraer; Frans C de Schryver; Joseph Klafter
Journal:  Proc Natl Acad Sci U S A       Date:  2005-02-04       Impact factor: 11.205

5.  Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited.

Authors:  Brian P English; Wei Min; Antoine M van Oijen; Kang Taek Lee; Guobin Luo; Hongye Sun; Binny J Cherayil; S C Kou; X Sunney Xie
Journal:  Nat Chem Biol       Date:  2005-12-25       Impact factor: 15.040

6.  When does the Michaelis-Menten equation hold for fluctuating enzymes?

Authors:  Wei Min; Irina V Gopich; Brian P English; S C Kou; X Sunney Xie; Attila Szabo
Journal:  J Phys Chem B       Date:  2006-10-19       Impact factor: 2.991

Review 7.  Optical detection of single molecules.

Authors:  S Nie; R N Zare
Journal:  Annu Rev Biophys Biomol Struct       Date:  1997

8.  Free-energy profile of the reaction catalyzed by triosephosphate isomerase.

Authors:  W J Albery; J R Knowles
Journal:  Biochemistry       Date:  1976-12-14       Impact factor: 3.162

9.  Single-molecule sequencing of an individual human genome.

Authors:  Dmitry Pushkarev; Norma F Neff; Stephen R Quake
Journal:  Nat Biotechnol       Date:  2009-08-10       Impact factor: 54.908

10.  Single-molecule enzymatic dynamics.

Authors:  H P Lu; L Xun; X S Xie
Journal:  Science       Date:  1998-12-04       Impact factor: 47.728

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  3 in total

1.  Structural conditions on complex networks for the Michaelis-Menten input-output response.

Authors:  Felix Wong; Annwesha Dutta; Debashish Chowdhury; Jeremy Gunawardena
Journal:  Proc Natl Acad Sci U S A       Date:  2018-09-07       Impact factor: 11.205

2.  Statistics and Related Topics in Single-Molecule Biophysics.

Authors:  Hong Qian; S C Kou
Journal:  Annu Rev Stat Appl       Date:  2014-01-01       Impact factor: 5.810

3.  Stepwise Signal Extraction via Marginal Likelihood.

Authors:  Chao Du; Chu-Lan Michael Kao; S C Kou
Journal:  J Am Stat Assoc       Date:  2015-02-06       Impact factor: 5.033

  3 in total

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