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Journal articles on the topic 'Bias Exchange Metadynamics'

Author: Grafiati
Published: 14 March 2023

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1

Baftizadeh Baghal, Fahimeh, Xevi Biarnes, Fabio Pietrucci, Alessandro Laio, and Fabio Affinito. "Simulation of Amyloid Nucleation with Bias-Exchange Metadynamics." Biophysical Journal 102, no. 3 (January 2012): 242a. http://dx.doi.org/10.1016/j.bpj.2011.11.1336.

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2

Singh, Richa, Rohit Bansal, Anurag Singh Rathore, and Gaurav Goel. "Equilibrium Ensembles for Insulin Folding from Bias-Exchange Metadynamics." Biophysical Journal 112, no. 8 (April 2017): 1571–85. http://dx.doi.org/10.1016/j.bpj.2017.03.015.

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3

Bulo, Rosa E., Hans Van Schoot, Daniel Rohr, and Carine Michel. "Bias-exchange metadynamics applied to the study of chemical reactivity." International Journal of Quantum Chemistry 110, no. 12 (March 10, 2010): 2299–307. http://dx.doi.org/10.1002/qua.22554.

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4

Baftizadeh, Fahimeh, Pilar Cossio, Fabio Pietrucci, and Alessandro Laio. "Protein Folding and Ligand-Enzyme Binding from Bias-Exchange Metadynamics Simulations." Current Physical Chemistry 2, no. 1 (January 1, 2012): 79–91. http://dx.doi.org/10.2174/1877946811202010079.

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5

Baftizadeh, Fahimeh, Pilar Cossio, Fabio Pietrucci, and Alessandro Laio. "Protein Folding and Ligand-Enzyme Binding from Bias-Exchange Metadynamics Simulations." Current Physical Chemistrye 2, no. 1 (January 1, 2012): 79–91. http://dx.doi.org/10.2174/1877947611202010079.

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6

Cao, Zanxia, Yunqiang Bian, Guodong Hu, Liling Zhao, Zhenzhen Kong, Yuedong Yang, Jihua Wang, and Yaoqi Zhou. "Bias-Exchange Metadynamics Simulation of Membrane Permeation of 20 Amino Acids." International Journal of Molecular Sciences 19, no. 3 (March 16, 2018): 885. http://dx.doi.org/10.3390/ijms19030885.

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7

Todorova, Nevena, Fabrizio Marinelli, Stefano Piana, and Irene Yarovsky. "Exploring the Folding Free Energy Landscape of Insulin Using Bias Exchange Metadynamics." Journal of Physical Chemistry B 113, no. 11 (March 19, 2009): 3556–64. http://dx.doi.org/10.1021/jp809776v.

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8

Furini, Simone, Paolo Barbini, and Carmen Domene. "Conduction and Selectivity in Na+ Channels Analyzed by Bias-Exchange Metadynamics Simulations." Biophysical Journal 108, no. 2 (January 2015): 490a. http://dx.doi.org/10.1016/j.bpj.2014.11.2682.

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9

Mehrabian, Hadi, and Bernhardt L. Trout. "In Silico Engineering of Hydrate Anti-agglomerant Molecules Using Bias-Exchange Metadynamics Simulations." Journal of Physical Chemistry C 124, no. 35 (August 5, 2020): 18983–92. http://dx.doi.org/10.1021/acs.jpcc.0c03251.

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10

Ansari, Samiul M., Andrea Coletta, Katrine Kirkeby Skeby, Jesper Sørensen, Birgit Schiøtt, and David S. Palmer. "Allosteric-Activation Mechanism of Bovine Chymosin Revealed by Bias-Exchange Metadynamics and Molecular Dynamics Simulations." Journal of Physical Chemistry B 120, no. 40 (September 29, 2016): 10453–62. http://dx.doi.org/10.1021/acs.jpcb.6b07491.

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11

Zerze, Gül H., Cayla M. Miller, Daniele Granata, and Jeetain Mittal. "Free Energy Surface of an Intrinsically Disordered Protein: Comparison between Temperature Replica Exchange Molecular Dynamics and Bias-Exchange Metadynamics." Journal of Chemical Theory and Computation 11, no. 6 (May 12, 2015): 2776–82. http://dx.doi.org/10.1021/acs.jctc.5b00047.

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12

Cao, Zanxia, Xiumei Zhang, Chunling Wang, Lei Liu, Liling Zhao, Jihua Wang, and Yaoqi Zhou. "Different effects of cholesterol on membrane permeation of arginine and tryptophan revealed by bias-exchange metadynamics simulations." Journal of Chemical Physics 150, no. 8 (February 28, 2019): 084106. http://dx.doi.org/10.1063/1.5082351.

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13

Domene, Carmen, Paolo Barbini, and Simone Furini. "Bias-Exchange Metadynamics Simulations: An Efficient Strategy for the Analysis of Conduction and Selectivity in Ion Channels." Journal of Chemical Theory and Computation 11, no. 4 (March 17, 2015): 1896–906. http://dx.doi.org/10.1021/ct501053x.

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14

Cossio, Pilar, Fabrizio Marinelli, Alessandro Laio, and Fabio Pietrucci. "Optimizing the Performance of Bias-Exchange Metadynamics: Folding a 48-Residue LysM Domain Using a Coarse-Grained Model." Journal of Physical Chemistry B 114, no. 9 (March 11, 2010): 3259–65. http://dx.doi.org/10.1021/jp907464b.

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15

Scarabelli, Guido, Davide Provasi, Ana Negri, and Marta Filizola. "Mapping the Conformational Free-Energy Landscape of Classical Opioid Peptides using Extensive Molecular Dynamics and Bias Exchange Metadynamics Simulations." Biophysical Journal 104, no. 2 (January 2013): 116a. http://dx.doi.org/10.1016/j.bpj.2012.11.672.

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16

Cao, Zanxia, Lei Liu, Guodong Hu, Yunqiang Bian, Haiyan Li, Jihua Wang, and Yaoqi Zhou. "Interplay of hydrophobic and hydrophilic interactions in sequence-dependent cell penetration of spontaneous membrane-translocating peptides revealed by bias-exchange metadynamics simulations." Biochimica et Biophysica Acta (BBA) - Biomembranes 1862, no. 10 (October 2020): 183402. http://dx.doi.org/10.1016/j.bbamem.2020.183402.

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17

Sridhar, Akshay, Stephen E. Farr, Guillem Portella, Tamar Schlick, Modesto Orozco, and Rosana Collepardo-Guevara. "Emergence of chromatin hierarchical loops from protein disorder and nucleosome asymmetry." Proceedings of the National Academy of Sciences 117, no. 13 (March 12, 2020): 7216–24. http://dx.doi.org/10.1073/pnas.1910044117.

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Abstract:
Protein flexibility and disorder is emerging as a crucial modulator of chromatin structure. Histone tail disorder enables transient binding of different molecules to the nucleosomes, thereby promoting heterogeneous and dynamic internucleosome interactions and making possible recruitment of a wide-range of regulatory and remodeling proteins. On the basis of extensive multiscale modeling we reveal the importance of linker histone H1 protein disorder for chromatin hierarchical looping. Our multiscale approach bridges microsecond-long bias-exchange metadynamics molecular dynamics simulations of atomistic 211-bp nucleosomes with coarse-grained Monte Carlo simulations of 100-nucleosome systems. We show that the long C-terminal domain (CTD) of H1—a ubiquitous nucleosome-binding protein—remains disordered when bound to the nucleosome. Notably, such CTD disorder leads to an asymmetric and dynamical nucleosome conformation that promotes chromatin structural flexibility and establishes long-range hierarchical loops. Furthermore, the degree of condensation and flexibility of H1 can be fine-tuned, explaining chromosomal differences of interphase versus metaphase states that correspond to partial and hyperphosphorylated H1, respectively. This important role of H1 protein disorder in large-scale chromatin organization has a wide range of biological implications.
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18

Paloncýová, Markéta, Veronika Navrátilová, Karel Berka, Alessandro Laio, and Michal Otyepka. "Role of Enzyme Flexibility in Ligand Access and Egress to Active Site: Bias-Exchange Metadynamics Study of 1,3,7-Trimethyluric Acid in Cytochrome P450 3A4." Journal of Chemical Theory and Computation 12, no. 4 (March 23, 2016): 2101–9. http://dx.doi.org/10.1021/acs.jctc.6b00075.

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19

Lu, Xiaoli, and Jing Huang. "A thermodynamic investigation of amyloid precursor protein processing by human γ-secretase." Communications Biology 5, no. 1 (August 18, 2022). http://dx.doi.org/10.1038/s42003-022-03818-7.

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Abstract:
AbstractHuman γ-secretase cleaves the transmembrane domains (TMDs) of amyloid precursor protein (APP) into pathologically relevant amyloid-β peptides (Aβs). The detailed mechanisms of the unique endoproteolytic cleavage by the presenilin 1 domain (PS1) of γ-secretase are still poorly understood. Herein, we provide thermodynamic insights into how the α-helical APP TMD is processed by γ-secretase and elucidate the specificity of Aβ48/Aβ49 cleavage using unbiased molecular dynamics and bias-exchange metadynamics simulations. The thermodynamic data show that the unwinding of APP TMD is driven by water hydration in the intracellular pocket of PS1, and the scissile bond T32-L33 or L33-V34 of the APP TMD can slide down and up to interact with D257/D385 to achieve endoproteolysis. In the wild-type system, the L33-V34 scissile bond is more easily hijacked by D257/D385 than T32-L33, resulting in higher Aβ49 cleavage, while the T32N mutation on the APP TMD decreases the energy barrier of the sliding of the scissile bonds and increases the hydrogen bond occupancy for Aβ48 cleavage. In summary, the thermodynamic analysis elucidates possible mechanisms of APP TMD processing by PS1, which might facilitate rational drug design targeting γ-secretase.
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