Literature DB >> 11340662

Multiple protein folding nuclei and the transition state ensemble in two-state proteins.

D K Klimov1, D Thirumalai.   

Abstract

Using exhaustive simulations of lattice models with side-chains, we show that optimized two-state folders reach the native state by a nucleation-collapse mechanism with multiple folding nuclei (MFN). For both the full model and the Go version, there are certain contacts that on an average participate in the critical nuclei with higher probability than the others. The high- (> or = 0.5) probability contacts are largely determined by the structure of the native state. Comparison of the results for the full sequence and the Go model shows that non-native interactions compromise the degree of cooperativity and stability of the native state. From an extremely detailed analysis of the folding kinetics, we find that non-native interactions are present in the folding nuclei. The folding times decrease if the non-native interactions in the folding nuclei are made neutral or repulsive. Using cluster analysis and making no prior assumption about reaction coordinate, we show that both full and Go models have three distinct transition states that give a structural description for the MFN. In the transition states, on an average, about two-thirds of the sequence is structured, whereas the rest is disordered, reminiscent of the polarized transition state in the SH3 domain. Our studies show that Go models cannot describe the transition state characteristics of two-state folders at the molecular level. As a byproduct of our investigations, we establish that our method of computing the transition state ensemble is numerically equivalent to the technique based on the stochastic separatrix, which also does not require a priori knowledge of the folding reaction coordinate. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11340662     DOI: 10.1002/prot.1058

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  37 in total

1.  Constructing, verifying, and dissecting the folding transition state of chymotrypsin inhibitor 2 with all-atom simulations.

Authors:  L Li; E I Shakhnovich
Journal:  Proc Natl Acad Sci U S A       Date:  2001-10-23       Impact factor: 11.205

2.  Exploring protein aggregation and self-propagation using lattice models: phase diagram and kinetics.

Authors:  R I Dima; D Thirumalai
Journal:  Protein Sci       Date:  2002-05       Impact factor: 6.725

3.  Compaction and tensile forces determine the accuracy of folding landscape parameters from single molecule pulling experiments.

Authors:  Greg Morrison; Changbong Hyeon; Michael Hinczewski; D Thirumalai
Journal:  Phys Rev Lett       Date:  2011-03-29       Impact factor: 9.161

4.  Fast protein folding kinetics.

Authors:  Jack Schonbrun; Ken A Dill
Journal:  Proc Natl Acad Sci U S A       Date:  2003-10-20       Impact factor: 11.205

5.  Simulations of beta-hairpin folding confined to spherical pores using distributed computing.

Authors:  D K Klimov; D Newfield; D Thirumalai
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

6.  Topological determinants of protein folding.

Authors:  Nikolay V Dokholyan; Lewyn Li; Feng Ding; Eugene I Shakhnovich
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-25       Impact factor: 11.205

7.  Insights into nucleic acid conformational dynamics from massively parallel stochastic simulations.

Authors:  Eric J Sorin; Young Min Rhee; Bradley J Nakatani; Vijay S Pande
Journal:  Biophys J       Date:  2003-08       Impact factor: 4.033

8.  Direct molecular dynamics observation of protein folding transition state ensemble.

Authors:  Feng Ding; Nikolay V Dokholyan; Sergey V Buldyrev; H Eugene Stanley; Eugene I Shakhnovich
Journal:  Biophys J       Date:  2002-12       Impact factor: 4.033

9.  Fast-folding protein kinetics, hidden intermediates, and the sequential stabilization model.

Authors:  S Banu Ozkan; Ken A Dill; Ivet Bahar
Journal:  Protein Sci       Date:  2002-08       Impact factor: 6.725

10.  What can one learn from experiments about the elusive transition state?

Authors:  Iksoo Chang; Marek Cieplak; Jayanth R Banavar; Amos Maritan
Journal:  Protein Sci       Date:  2004-08-04       Impact factor: 6.725

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