Markov modeling of peptide folding in the presence of protein crowders

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Abstract

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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Jülich Research Centre
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Annan fysik
  • Biofysik
Originalspråkengelska
Artikelnummer055101
TidskriftJournal of Chemical Physics
Volym148
Utgivningsnummer5
StatusPublished - 2018 feb 7
PublikationskategoriForskning
Peer review utfördJa