Literature DB >> 21174461

Atomistic folding simulations of the five-helix bundle protein λ(6−85).

Gregory R Bowman1, Vincent A Voelz, Vijay S Pande.   

Abstract

Protein folding is a classic grand challenge that is relevant to numerous human diseases, such as protein misfolding diseases like Alzheimer’s disease. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment, with theory and simulation providing an atomically detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, theory and simulation generally fall orders of magnitude short of biologically relevant time scales. Here we report significant progress toward closing this gap: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein with explicit solvent that captures dynamics on a 10 milliseconds time scale. In addition, we provide a number of predictions that warrant further experimental investigation. For example, our model’s native state is a kinetic hub, and biexponential kinetics arises from the presence of many free-energy basins separated by barriers of different heights rather than a single low barrier along one reaction coordinate (the previously proposed incipient downhill folding scenario).

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Year:  2011        PMID: 21174461      PMCID: PMC3043158          DOI: 10.1021/ja106936n

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  25 in total

1.  On the extended beta-conformation propensity of polypeptides at high temperature.

Authors:  Wei Yuan Yang; Edgar Larios; Martin Gruebele
Journal:  J Am Chem Soc       Date:  2003-12-31       Impact factor: 15.419

2.  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

3.  Protein-folding dynamics.

Authors:  M Karplus; D L Weaver
Journal:  Nature       Date:  1976-04-01       Impact factor: 49.962

4.  Using generalized ensemble simulations and Markov state models to identify conformational states.

Authors:  Gregory R Bowman; Xuhui Huang; Vijay S Pande
Journal:  Methods       Date:  2009-05-04       Impact factor: 3.608

5.  Progress and challenges in the automated construction of Markov state models for full protein systems.

Authors:  Gregory R Bowman; Kyle A Beauchamp; George Boxer; Vijay S Pande
Journal:  J Chem Phys       Date:  2009-09-28       Impact factor: 3.488

6.  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

7.  Enhanced modeling via network theory: Adaptive sampling of Markov state models.

Authors:  Gregory R Bowman; Daniel L Ensign; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2010       Impact factor: 6.006

8.  Submillisecond folding of monomeric lambda repressor.

Authors:  G S Huang; T G Oas
Journal:  Proc Natl Acad Sci U S A       Date:  1995-07-18       Impact factor: 11.205

9.  Elucidating the folding problem of helical peptides using empirical parameters.

Authors:  V Muñoz; L Serrano
Journal:  Nat Struct Biol       Date:  1994-06

10.  The operator-binding domain of lambda repressor: structure and DNA recognition.

Authors:  C O Pabo; M Lewis
Journal:  Nature       Date:  1982-07-29       Impact factor: 49.962

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  50 in total

1.  Calculation of rate spectra from noisy time series data.

Authors:  Vincent A Voelz; Vijay S Pande
Journal:  Proteins       Date:  2011-11-17

2.  Characterization and rapid sampling of protein folding Markov state model topologies.

Authors:  Jeffrey K Weber; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2011-10-11       Impact factor: 6.006

3.  Protein folding is mechanistically robust.

Authors:  Jeffrey K Weber; Vijay S Pande
Journal:  Biophys J       Date:  2012-02-21       Impact factor: 4.033

4.  Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites.

Authors:  Gregory R Bowman; Phillip L Geissler
Journal:  Proc Natl Acad Sci U S A       Date:  2012-07-02       Impact factor: 11.205

Review 5.  Taming the complexity of protein folding.

Authors:  Gregory R Bowman; Vincent A Voelz; Vijay S Pande
Journal:  Curr Opin Struct Biol       Date:  2011-02       Impact factor: 6.809

6.  The fast and the slow: folding and trapping of λ6-85.

Authors:  Maxim B Prigozhin; Martin Gruebele
Journal:  J Am Chem Soc       Date:  2011-11-14       Impact factor: 15.419

7.  Native contacts determine protein folding mechanisms in atomistic simulations.

Authors:  Robert B Best; Gerhard Hummer; William A Eaton
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-15       Impact factor: 11.205

8.  Revealing what gets buried first in protein folding.

Authors:  Tobin R Sosnick; Michael C Baxa
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-04       Impact factor: 11.205

9.  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

10.  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

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