Literature DB >> 15381433

Optimal combination of theory and experiment for the characterization of the protein folding landscape of S6: how far can a minimalist model go?

Silvina Matysiak1, Cecilia Clementi.   

Abstract

The detailed characterization of the overall free energy landscape associated with the folding process of a protein is the ultimate goal in protein folding studies. Modern experimental techniques provide accurate thermodynamic and kinetic measurements on restricted regions of a protein landscape. Although simplified protein models can access larger regions of the landscape, they are oftentimes built on assumptions and approximations that affect the accuracy of the results. We present a new methodology that allows to combine the complementary strengths of theory and experiment for a more complete characterization of a protein folding landscape. We prove that this new procedure allows a simplified protein model to reproduce remarkably well (correlation coefficient > 0.9) all experimental data available on free energies differences upon single mutations for S6 ribosomal protein and two circular permutants. Our results confirm and quantify the hypothesis, recently formulated on the basis of experimental data, that the folding landscape of protein S6 is strongly affected by an atypical distribution of contact energies.

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Year:  2004        PMID: 15381433     DOI: 10.1016/j.jmb.2004.08.006

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  20 in total

1.  Constructing sequence-dependent protein models using coevolutionary information.

Authors:  Ryan R Cheng; Mohit Raghunathan; Jeffrey K Noel; José N Onuchic
Journal:  Protein Sci       Date:  2015-08-10       Impact factor: 6.725

2.  The experimental folding landscape of monomeric lactose repressor, a large two-domain protein, involves two kinetic intermediates.

Authors:  Corey J Wilson; Payel Das; Cecilia Clementi; Kathleen S Matthews; Pernilla Wittung-Stafshede
Journal:  Proc Natl Acad Sci U S A       Date:  2005-10-03       Impact factor: 11.205

3.  Characterization of the folding landscape of monomeric lactose repressor: quantitative comparison of theory and experiment.

Authors:  Payel Das; Corey J Wilson; Giovanni Fossati; Pernilla Wittung-Stafshede; Kathleen S Matthews; Cecilia Clementi
Journal:  Proc Natl Acad Sci U S A       Date:  2005-10-03       Impact factor: 11.205

4.  Balancing energy and entropy: a minimalist model for the characterization of protein folding landscapes.

Authors:  Payel Das; Silvina Matysiak; Cecilia Clementi
Journal:  Proc Natl Acad Sci U S A       Date:  2005-07-08       Impact factor: 11.205

5.  Identification of the minimal protein-folding nucleus through loop-entropy perturbations.

Authors:  Magnus O Lindberg; Ellinor Haglund; Isaac A Hubner; Eugene I Shakhnovich; Mikael Oliveberg
Journal:  Proc Natl Acad Sci U S A       Date:  2006-02-27       Impact factor: 11.205

6.  Sequence of events in folding mechanism: beyond the Gō model.

Authors:  Ludovico Sutto; Guido Tiana; Ricardo A Broglia
Journal:  Protein Sci       Date:  2006-07       Impact factor: 6.725

7.  Stability and kinetic properties of C5-domain from myosin binding protein C and its mutants.

Authors:  Carlo Guardiani; Fabio Cecconi; Roberto Livi
Journal:  Biophys J       Date:  2007-10-26       Impact factor: 4.033

8.  The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models.

Authors:  W G Noid; Pu Liu; Yanting Wang; Jhih-Wei Chu; Gary S Ayton; Sergei Izvekov; Hans C Andersen; Gregory A Voth
Journal:  J Chem Phys       Date:  2008-06-28       Impact factor: 3.488

9.  Analyzing pathogenic mutations of C5 domain from cardiac myosin binding protein C through MD simulations.

Authors:  Fabio Cecconi; Carlo Guardiani; Roberto Livi
Journal:  Eur Biophys J       Date:  2008-04-01       Impact factor: 1.733

10.  Analysis of the free-energy surface of proteins from reversible folding simulations.

Authors:  Lucy R Allen; Sergei V Krivov; Emanuele Paci
Journal:  PLoS Comput Biol       Date:  2009-07-10       Impact factor: 4.475

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