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Anders Irbäck. Photo.

Anders Irbäck

Professor

Anders Irbäck. Photo.

Markov modeling of peptide folding in the presence of protein crowders

Author

  • Daniel Nilsson
  • Sandipan Mohanty
  • Anders Irbäck

Summary, in English

We use Markov state models (MSMs) to analyze the dynamics of a β-hairpin-forming peptide in Monte Carlo (MC) simulations with interacting protein crowders, for two different types of crowder proteins [bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the temperature used, the peptide can be folded or unfolded and bound or unbound to crowder molecules. Four or five major free-energy minima can be identified. To estimate the dominant MC relaxation times of the peptide, we build MSMs using a range of different time resolutions or lag times. We show that stable relaxation-time estimates can be obtained from the MSM eigenfunctions through fits to autocorrelation data. The eigenfunctions remain sufficiently accurate to permit stable relaxation-time estimation down to small lag times, at which point simple estimates based on the corresponding eigenvalues have large systematic uncertainties. The presence of the crowders has a stabilizing effect on the peptide, especially with BPTI crowders, which can be attributed to a reduced unfolding rate ku, while the folding rate kf is left largely unchanged.

Department/s

  • Computational Biology and Biological Physics
  • eSSENCE: The e-Science Collaboration

Publishing year

2018-02-07

Language

English

Publication/Series

Journal of Chemical Physics

Volume

148

Issue

5

Document type

Journal article

Publisher

American Institute of Physics (AIP)

Topic

  • Other Physics Topics
  • Biophysics

Status

Published

ISBN/ISSN/Other

  • ISSN: 0021-9606