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• W3098955055 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." @default.
• W3098955055 created "2020-11-23" @default.
• W3098955055 creator A5050478272 @default.
• W3098955055 creator A5060442147 @default.
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• W3098955055 date "2018-02-01" @default.
• W3098955055 modified "2023-09-24" @default.
• W3098955055 title "Markov modeling of peptide folding in the presence of protein crowders" @default.
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• W3098955055 doi "https://doi.org/10.1063/1.5017031" @default.
• W3098955055 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29421894" @default.
• W3098955055 hasPublicationYear "2018" @default.
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