scholarly journals A restrained locally enhanced sampling method (RLES) for finding free energy minima in complex systems

2020 ◽  
Vol 153 (12) ◽  
pp. 121103
Author(s):  
Victor Ovchinnikov ◽  
Simone Conti ◽  
Martin Karplus
Keyword(s):  
Free Energy ◽  
Complex Systems ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Locally Enhanced Sampling

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 2P128 Structure analysis of DNA mini hairpin using locally enhanced sampling method

2005 ◽  
Vol 45 (supplement) ◽  
pp. S151
Author(s):  
S. Yamasaki ◽  
S. Nakamura ◽  
T. Terada ◽  
K. Shimizu
Keyword(s):  
Structure Analysis ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Locally Enhanced Sampling

scholarly journals Chasing the full free energy landscape of neuroreceptor/ligand unbinding by metadynamics simulations

2019 ◽  
Author(s):  
Riccardo Capelli ◽  
Anna Bochicchio ◽  
GiovanniMaria Piccini ◽  
Rodrigo Casasnovas ◽  
Paolo Carloni ◽  
...  
Keyword(s):  
Free Energy ◽  
Sampling Method ◽  
Energy Landscape ◽  
Membrane Receptors ◽  
Free Energy Landscape ◽  
Neuronal Membrane ◽  
Allosteric Modulator ◽  
Enhanced Sampling ◽  
Reaction Coordinates ◽  
Slowing Down

Predicting the complete free energy landscape associated with protein-ligand unbinding would greatly help designing drugs with highly optimized pharmacokinetics. Here we investigate the unbinding of the iperoxo agonist to its target human neuroreceptor M2, embedded in a neuronal membrane. By feeding out-of-equilibrium molecular simulations data in a classification analysis, we identify the few essential reaction coordinates of the process. The full landscape is then reconstructed using an exact enhanced sampling method, well-tempered metadynamics in its funnel variant. The calculations reproduce well the measured affinity, provide a rationale for mutagenesis data and show that the ligand can escape via two different routes. The allosteric modulator LY2119620 turns out to hamper both escapes routes, thus slowing down the unbinding process, as experimentally observed. This computationally affordable protocol is totally general and it can be easily applied to determine the full free energy landscape of membrane receptors/drug interactions.

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