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|a 10.1002/jcc.27059
|2 doi
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|a pubmed24n1169.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Zschau, Richard L
|e verfasserin
|4 aut
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|a Mechanism of β-hairpin formation in AzoChignolin and Chignolin
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 27.02.2023
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|a Date Revised 31.03.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
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|a AzoChignolin is a photoswitchable variant of the mini-protein Chignolin with an azobenzene (AMPP) replacing the central loop. AzoChignolin is unfolded with AMPP in the trans-isomer. Transition to the cis-isomer causes β-hairpin folding similar to Chignolin. The AzoChignolin system is excellently suited for comprehensive analysis of folding nucleation kinetics. Utilizing multiple long-time MD simulations of AzoChignolin and Chignolin in MeOH and water, we estimated Markov models to examine folding kinetics of both peptides. We show that while AzoChignolin mimics Chignolin's structure well, the folding kinetics are quite different. Not only folding times but also intermediate states differ, particularly Chignolin is able to fold in MeOH into an α-helical intermediate which is impossible to form in AzoChignolin. The Markov models demonstrate that AzoChignolin's kinetics are generally faster, specifically when comparing the two main microfolding processes of hydrophobic collapse and turn formation. Photoswitchable loops are used frequently to understand the kinetics of elementary protein folding nucleation. However, our results indicate that intermediates and folding kinetics may differ between natural loops and photoswitchable variants
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Markov state models
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|a molecular dynamics
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|a protein kinetics
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|a single-protein analysis
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|a β-Hairpin
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|a chignolin
|2 NLM
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|a Oligopeptides
|2 NLM
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|a Peptides
|2 NLM
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|a Zacharias, Martin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 44(2023), 9 vom: 05. Apr., Seite 988-1001
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:44
|g year:2023
|g number:9
|g day:05
|g month:04
|g pages:988-1001
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|u http://dx.doi.org/10.1002/jcc.27059
|3 Volltext
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