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Protein folding by distributed computing and the denatured state ensemble.

Neelan J Marianayagam1, Nicolas L Fawzi, Teresa Head-Gordon

  • 1Department of Bioengineering and UCSF/UCB Joint Graduate Group in Bioengineering, University of California, Berkeley, CA 94720, USA.

Proceedings of the National Academy of Sciences of the United States of America
|November 4, 2005
PubMed
Summary

Distributed computing (DC) using folding@home (FH) simulations can provide insights into protein folding kinetics. However, short, uncoupled simulations may yield unphysical results and misrepresent equilibrium folding pathways.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Protein Dynamics

Background:

  • Distributed computing (DC) and folding@home (FH) are utilized for protein folding kinetics studies.
  • Previous studies show agreement with experimental rates but inconsistencies in folding pathways.

Purpose of the Study:

  • To rigorously test the FH protocol using a coarse-grain model of protein L.
  • To evaluate the accuracy of short-time, uncoupled folding simulations against detailed equilibrium characterizations.

Main Methods:

  • Utilized a coarse-grain model of protein L with well-characterized two-state kinetics.
  • Performed approximately 10,000 short-time, uncoupled folding simulations using the FH protocol.
  • Initiated simulations from an extended state and an equilibrated denatured state ensemble (DSE).

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Main Results:

  • FH simulations produced non-Poisson distributions and unphysical early folding events.
  • Longer folding events showed a correct folding barrier but were not representative of the equilibrium ensemble.
  • Simulations from an equilibrated DSE also failed to match equilibrium kinetics due to overrepresented high-energy subpopulations.

Conclusions:

  • The DC approach with sufficient simulation time from an equilibrated DSE can align with experimental rates and mechanisms.
  • However, DC combined with advanced experiments can reveal limitations of the two-state approximation by observing folding from higher-energy states.