Markov modeling of peptide folding in the presence of protein crowders

Nilsson, Daniel; Irbäck, Anders; Mohanty, Sandipan

doi:10.1063/1.5017031

Journal Article

FZJ-2018-01216

Markov modeling of peptide folding in the presence of protein crowders

Nilsson, D. ; Mohanty, S.FZJ* ; Irbäck, A. (Corresponding author)Extern*

2018
American Institute of Physics Melville, NY

The journal of chemical physics 148(5), 055101 (2018) [10.1063/1.5017031]

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Please use a persistent id in citations: http://hdl.handle.net/2128/17398 doi:10.1063/1.5017031

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.

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