Markov modeling of peptide folding in the presence of protein crowders
Nilsson, Daniel LU ; Mohanty, Sandipan LU and Irbäck, Anders LU
(2018)
In Journal of Chemical Physics
148(5).
- 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... (More)
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.
(Less)
- author
- Nilsson, Daniel
LU
; Mohanty, Sandipan
LU
and Irbäck, Anders
LU
- organization
- publishing date
- 2018-02-07
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Chemical Physics
- volume
- 148
- issue
- 5
- article number
- 055101
- publisher
- American Institute of Physics (AIP)
- external identifiers
-
- scopus:85041418662
- pmid:29421894
- ISSN
- 0021-9606
- DOI
- 10.1063/1.5017031
- language
- English
- LU publication?
- yes
- id
- 5a7bf6e8-6e8d-4069-9d98-5aa406427a47
- date added to LUP
- 2018-02-15 12:30:16
- date last changed
- 2024-04-19 02:53:19
@article{5a7bf6e8-6e8d-4069-9d98-5aa406427a47,
abstract = {{<p>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<sub>u</sub>, while the folding rate k<sub>f</sub> is left largely unchanged.</p>}},
author = {{Nilsson, Daniel and Mohanty, Sandipan and Irbäck, Anders}},
issn = {{0021-9606}},
language = {{eng}},
month = {{02}},
number = {{5}},
publisher = {{American Institute of Physics (AIP)}},
series = {{Journal of Chemical Physics}},
title = {{Markov modeling of peptide folding in the presence of protein crowders}},
url = {{http://dx.doi.org/10.1063/1.5017031}},
doi = {{10.1063/1.5017031}},
volume = {{148}},
year = {{2018}},
}