Markov modeling of peptide folding in the presence of protein crowders

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 k_u, while the folding rate k_f is left largely unchanged.

• Author: Daniel Nilsson; Sandipan Mohanty; Anders Irbäck
• Department/s: Computational Biology and Biological Physics - Has been reorganised; Computational Science for Health and Environment; eSSENCE: The e-Science Collaboration
• Publishing year: 2018-02-07
• Language: English
• Publication/Series: Journal of Chemical Physics
• Volume: 148
• Issue: 5
• Links: http://dx.doi.org/10.1063/1.5017031
• Document type: Journal article
• Publisher: American Institute of Physics (AIP)
• Topic: Other Physics Topics; Biophysics
• Status: Published
• Research group: Computational Science for Health and Environment
• ISBN/ISSN/Other: ISSN: 0021-9606