Enhanced Sampling Methods for Atomistic Simulation of Nucleic Acids

2006 ◽  
pp. 147-167 ◽  
Author(s):  
Catherine Kelso ◽  
Carlos Simmerling
Keyword(s):  
Nucleic Acids ◽  
Atomistic Simulation ◽  
Sampling Methods ◽  
Enhanced Sampling

scholarly journals Enhanced Sampling and Overfitting Analyses in Structural Refinement of Nucleic Acids into Electron Microscopy Maps

2013 ◽  
Vol 117 (14) ◽  
pp. 3738-3746 ◽  
Author(s):  
Harish Vashisth ◽  
Georgios Skiniotis ◽  
Charles L. Brooks
Keyword(s):  
Electron Microscopy ◽  
Nucleic Acids ◽  
Enhanced Sampling ◽  
Structural Refinement

scholarly journals Neural networks-based variationally enhanced sampling

2019 ◽  
Vol 116 (36) ◽  
pp. 17641-17647 ◽  
Author(s):  
Luigi Bonati ◽  
Yue-Yu Zhang ◽  
Michele Parrinello
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Atomistic Simulation ◽  
Sampling Method ◽  
Machine Learning Techniques ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Learning Techniques ◽  
Minimization Technique ◽  
Energy Surfaces

Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.

scholarly journals Substrate Binding Process and Mechanistic Functioning of Type 1 11β-Hydroxysteroid Dehydrogenase from Enhanced Sampling Methods

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. e25375 ◽  
Author(s):  
Angelo D. Favia ◽  
Matteo Masetti ◽  
Maurizio Recanatini ◽  
Andrea Cavalli
Keyword(s):  
Sampling Methods ◽  
Substrate Binding ◽  
Hydroxysteroid Dehydrogenase ◽  
Enhanced Sampling ◽  
Binding Process

scholarly journals Using Long-Timescale Molecular Dynamics Simulations to Benchmark Enhanced Sampling Methods

2015 ◽  
Vol 108 (2) ◽  
pp. 183a
Author(s):  
Albert C. Pan ◽  
Thomas M. Weinreich ◽  
Stefano Piana ◽  
David E. Shaw
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Sampling Methods ◽  
Enhanced Sampling ◽  
Dynamics Simulations

scholarly journals Kinetic Models of Enhanced Sampling Methods

2011 ◽  
Vol 100 (3) ◽  
pp. 210a
Author(s):  
Edina Rosta ◽  
Gerhard Hummer
Keyword(s):  
Kinetic Models ◽  
Sampling Methods ◽  
Enhanced Sampling
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