Enhanced sampling method in molecular simulations using genetic algorithm for biomolecular systems

2018 ◽  
Vol 40 (2) ◽  
pp. 475-481
Author(s):  
Yoshitake Sakae ◽  
John E. Straub ◽  
Yuko Okamoto
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Molecular Simulations ◽  
Enhanced Sampling ◽  
Biomolecular Systems

scholarly journals Biomolecular conformational changes and ligand binding: from kinetics to thermodynamics

2017 ◽  
Vol 8 (9) ◽  
pp. 6466-6473 ◽  
Author(s):  
Yong Wang ◽  
João Miguel Martins ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Molecular Simulations ◽  
Kinetic Properties ◽  
Enhanced Sampling ◽  
Biomolecular Systems

Biomolecular systems such as protein–ligand complexes are governed by thermodynamic and kinetic properties that may be estimated at the same time through enhanced-sampling molecular simulations.

Optimization of the OOP Children Restraint System

2012 ◽  
Vol 155-156 ◽  
pp. 386-390
Author(s):  
Zhong Hao Bai ◽  
Jing Fei ◽  
Wei Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Approximate Model ◽  
Parameters Optimization ◽  
Fe Model ◽  
Restraint System ◽  
Effective Protection ◽  
Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

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 A variational approach to nucleation simulation

2016 ◽  
Vol 195 ◽  
pp. 557-568 ◽  
Author(s):  
Pablo M. Piaggi ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Molecular Dynamics ◽  
Computer Simulation ◽  
Free Energy ◽  
Variational Approach ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Technical Problems ◽  
Lennard Jones ◽  
Dynamics Simulations

We study by computer simulation the nucleation of a supersaturated Lennard-Jones vapor into the liquid phase. The large free energy barriers to transition make the time scale of this process impossible to study by ordinary molecular dynamics simulations. Therefore we use a recently developed enhanced sampling method [Valsson and Parrinello, Phys. Rev. Lett.113, 090601 (2014)] based on the variational determination of a bias potential. We differ from previous applications of this method in that the bias is constructed on the basis of the physical model provided by the classical theory of nucleation. We examine the technical problems associated with this approach. Our results are very satisfactory and will pave the way for calculating the nucleation rates in many systems.

scholarly journals Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

2015 ◽  
Vol 3 ◽  
Author(s):  
Hiroshi Fujisaki ◽  
Kei Moritsugu ◽  
Yasuhiro Matsunaga ◽  
Tetsuya Morishita ◽  
Luca Maragliano
Keyword(s):  
Phase Space ◽  
Molecular Simulations ◽  
Extended Phase Space ◽  
Enhanced Sampling ◽  
Extended Phase ◽  
Phase Space Methods

scholarly journals Conformational ensemble of human α-synuclein physiological form predicted by molecular simulations

2016 ◽  
Vol 18 (8) ◽  
pp. 5702-5706 ◽  
Author(s):  
G. Rossetti ◽  
F. Musiani ◽  
E. Abad ◽  
D. Dibenedetto ◽  
H. Mouhib ◽  
...  
Keyword(s):  
Protein Dynamics ◽  
Molecular Simulations ◽  
Intramolecular Interactions ◽  
Conformational Ensemble ◽  
Enhanced Sampling ◽  
Post Translational Modification ◽  
Α Helix

Enhanced sampling simulations of N-terminally acetylated human α-synuclein suggest that the post-translational modification leads to the formation of a transient amphipathic α-helix altering protein dynamics at the N-terminal and intramolecular interactions.

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