scholarly journals Progress in deep Markov State Modeling: Coarse graining and experimental data restraints

2021 ◽  
Author(s):  
Andreas Mardt ◽  
Frank Noé
Keyword(s):  
Experimental Data ◽  
Coarse Graining ◽  
Markov State ◽  
Markov State Modeling

scholarly journals Markov state modeling and dynamical coarse-graining via discrete relaxation path sampling

2015 ◽  
Vol 143 (4) ◽  
pp. 044119 ◽  
Author(s):  
B. Fačkovec ◽  
E. Vanden-Eijnden ◽  
D. J. Wales
Keyword(s):  
Coarse Graining ◽  
Path Sampling ◽  
Markov State ◽  
Markov State Modeling

scholarly journals Targeting structural features of viral genomes with a nano-sized supramolecular drug

2021 ◽  
Author(s):  
Lazaros Melidis ◽  
Iain B. Styles ◽  
Michael J. Hannon
Keyword(s):  
Experimental Data ◽  
Md Simulations ◽  
Structural Features ◽  
Conformational Flexibility ◽  
Viral Genomes ◽  
Markov State ◽  
Mechanistic Insight ◽  
Tar Rna ◽  
Markov State Modeling ◽  
Insight Into

MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion.

scholarly journals Markov state modeling of sliding friction

2016 ◽  
Vol 94 (5) ◽  
Author(s):  
F. Pellegrini ◽  
François P. Landes ◽  
A. Laio ◽  
S. Prestipino ◽  
E. Tosatti
Keyword(s):  
Sliding Friction ◽  
Markov State ◽  
Markov State Modeling

scholarly journals A Model for the Simulation of the CnEm Nonionic Surfactant Family Derived from Recent Experimental Results

2020 ◽  
Author(s):  
Michael Johnston ◽  
Andrew Duff ◽  
Richard L. Anderson ◽  
William Swope
Keyword(s):  
Experimental Data ◽  
Computational Models ◽  
Nonionic Surfactants ◽  
Dissipative Particle Dynamics ◽  
Coarse Graining ◽  
Experimental Results ◽  
Important Class ◽  
Moderate Amount ◽  
Weighted Averages ◽  
Aggregation Numbers

<div><div><div><p>Using a comprehensive set of recently published experimental results for training and validation, we have developed computational models appropriate for simulations of aqueous solutions of poly(ethylene oxide) alkyl ethers, an important class of micelle- forming nonionic surfactants, usually denoted CnEm. These models are suitable for use in simulations that employ a moderate amount of coarse graining and especially for dissipative particle dynamics (DPD), which we adopt in this work.</p><p><br></p><p>The experimental data used for training and validation were reported earlier and produced in our laboratory using dynamic light scattering (DLS) measurements per- formed on twelve members of the CnEm compound family yielding micelle size dis- tribution functions and mass weighted mean aggregation numbers at each of several surfactant concentrations. The range of compounds and quality of the experimental results were designed to support the development of computational models. An es- sential feature of this work is that all simulation results were analysed in a way that is consistent with the experimental data. Proper account is taken of the fact that a broad distribution of micelle sizes exists, so mass weighted averages (rather than num- ber weighted averages) over this distribution are required for the proper comparison of simulation and experimental results.</p><p><br></p><p>The resulting DPD force field reproduces several important trends seen in the exper- imental critical micelle concentrations and mass averaged mean aggregation numbers with respect to surfactant characteristics and concentration. We feel it can be used to investigate a number of open questions regarding micelle sizes and shapes and their dependence on surfactant concentration for this important class of nonionic surfactants.</p></div></div></div>

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