scholarly journals Multiresolution Modeling of Semidilute Polymer Solutions: Coarse-Graining Using Wavelet-Accelerated Monte Carlo

Computation ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 44
Author(s):  
Animesh Agarwal ◽  
Brooks Rabideau ◽  
Ahmed Ismail
Keyword(s):  
Monte Carlo ◽  
Polymer Solutions ◽  
Coarse Graining ◽  
Multiresolution Modeling

Coarse-graining dipolar interactions in simple fluids and polymer solutions: Monte Carlo studies of the phase behavior

2009 ◽  
Vol 11 (12) ◽  
pp. 1923 ◽  
Author(s):  
B. M. Mognetti ◽  
P. Virnau ◽  
L. Yelash ◽  
W. Paul ◽  
K. Binder ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Phase Behavior ◽  
Polymer Solutions ◽  
Coarse Graining ◽  
Dipolar Interactions ◽  
Simple Fluids ◽  
Monte Carlo Studies

Crossover scaling in semidilute polymer solutions: a Monte Carlo test

1991 ◽  
Vol 1 (1) ◽  
pp. 37-60 ◽  
Author(s):  
Wolfgang Paul ◽  
Kurt Binder ◽  
Dieter W. Heermann ◽  
Kurt Kremer
Keyword(s):  
Monte Carlo ◽  
Polymer Solutions ◽  
Monte Carlo Test

Phase transitions of single polymer chains and of polymer solutions: insights from Monte Carlo simulations

2008 ◽  
Vol 20 (49) ◽  
pp. 494215 ◽  
Author(s):  
K Binder ◽  
W Paul ◽  
T Strauch ◽  
F Rampf ◽  
V Ivanov ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Phase Transitions ◽  
Monte Carlo Simulations ◽  
Polymer Solutions ◽  
Polymer Chains

scholarly journals Diffusive properties of solvent molecules in the neighborhood of a polymer chain as seen by Monte-Carlo simulations

Soft Matter ◽  
2016 ◽  
Vol 12 (25) ◽  
pp. 5519-5528 ◽  
Author(s):  
M. Kozanecki ◽  
K. Halagan ◽  
J. Saramak ◽  
K. Matyjaszewski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Chain Length ◽  
Polymer Chain ◽  
Polymer Solutions ◽  
Solvent Molecules

The influence of both polymer chain length and concentration on the mobility of solvent molecules in polymer solutions was studied by Monte Carlo simulations with the use of the dynamic lattice liquid (DLL) model.

scholarly journals Coarse-graining polymer solutions: A critical appraisal of single- and multi-site models

2015 ◽  
Vol 224 (12) ◽  
pp. 2239-2267 ◽  
Author(s):  
G. D’Adamo ◽  
R. Menichetti ◽  
A. Pelissetto ◽  
C. Pierleoni
Keyword(s):  
Critical Appraisal ◽  
Polymer Solutions ◽  
Coarse Graining

scholarly journals Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

2018 ◽  
Author(s):  
Yong Wang ◽  
Pengfei Tian ◽  
Wouter Boomsma ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Conformational Changes ◽  
Conformational Dynamics ◽  
Coarse Graining ◽  
Monte Carlo Sampling ◽  
Implicit Solvent ◽  
Great Success ◽  
Native State ◽  
Dynamics Simulations

AbstractEnergy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (Gō) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models which include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based Gō potential, and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi, and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22∗ force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials, while retaining the advantages of Gō models for sampling and guiding to the native state. We expect that the model will be widely applicable to study the folding of more complex proteins, or to study conformational dynamics and integration with experimental data.

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