Literature DB >> 26575573

Kinetic network models of tryptophan mutations in β-hairpins reveal the importance of non-native interactions.

Asghar M Razavi1, Vincent A Voelz1.   

Abstract

We present an analysis of the most extensive explicit-solvent simulations of β-hairpins to date (9.4 ms in aggregate), with the aim of probing the effects of tryptophan mutations on folding. From molecular simulations of GB1 hairpin, trpzip4, trpzip5, and trpzip6 performed on Folding@home, Markov State Models (MSMs) were constructed using a unified set of metastable states, enabling objective comparison of folding mechanisms. MSM models display quantitative agreement with experimental structural observables and folding kinetics, and predict multimodal kinetics due to specific non-native kinetic traps, which be identified as on- or off-pathway from the network topology. We quantify kinetic frustration by several means, including the s-ensemble method to evaluate glasslike behavior. Free-energy profiles and transition state movement clearly show stabilization of non-native states as Trp mutations are introduced. Remarkably, we find that "β-capped" sequences (trpzip4 and trpzip5) are able to overcome this frustration and remain cooperative two-state folders with a large time-scale gap. These results suggest that, while β-capping motifs are robust, fold stabilization by tryptophan generally may require overcoming significant non-native kinetic traps, perhaps explaining their under-representation in natural proteins.

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Year:  2015        PMID: 26575573     DOI: 10.1021/acs.jctc.5b00088

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  8 in total

1.  Characterizing a partially ordered miniprotein through folding molecular dynamics simulations: Comparison with the experimental data.

Authors:  Athanasios S Baltzis; Nicholas M Glykos
Journal:  Protein Sci       Date:  2015-12-16       Impact factor: 6.725

2.  Exposing the Nucleation Site in α-Helix Folding: A Joint Experimental and Simulation Study.

Authors:  Arusha Acharyya; Yunhui Ge; Haifan Wu; William F DeGrado; Vincent A Voelz; Feng Gai
Journal:  J Phys Chem B       Date:  2019-02-14       Impact factor: 2.991

3.  Mechanistic Kinetic Model Reveals How Amyloidogenic Hydrophobic Patches Facilitate the Amyloid-β Fibril Elongation.

Authors:  Hengyi Xie; Ana Rojas; Gia G Maisuradze; George Khelashvili
Journal:  ACS Chem Neurosci       Date:  2022-03-08       Impact factor: 4.418

4.  Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Authors:  Michael D Ward; Maxwell I Zimmerman; Artur Meller; Moses Chung; S J Swamidass; Gregory R Bowman
Journal:  Nat Commun       Date:  2021-05-21       Impact factor: 14.919

5.  Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking.

Authors:  Sudipto Mukherjee; George A Pantelopulos; Vincent A Voelz
Journal:  Sci Rep       Date:  2016-08-19       Impact factor: 4.379

6.  A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter.

Authors:  Asghar M Razavi; George Khelashvili; Harel Weinstein
Journal:  Sci Rep       Date:  2017-01-06       Impact factor: 4.379

7.  A molecular dynamics simulation study on the propensity of Asn-Gly-containing heptapeptides towards β-turn structures: Comparison with ab initio quantum mechanical calculations.

Authors:  Dimitrios A Mitsikas; Nicholas M Glykos
Journal:  PLoS One       Date:  2020-12-03       Impact factor: 3.240

8.  Granger Causality Analysis of Chignolin Folding.

Authors:  Marcin Sobieraj; Piotr Setny
Journal:  J Chem Theory Comput       Date:  2022-02-15       Impact factor: 6.006

  8 in total

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