Free-Energy Surface Prediction by Flying Gaussian Method
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague
DOI10.24435/materialscloud:2019.0032/v1 (version v1, submitted on 18 June 2019)
How to cite this entry
Vojtech Spiwok, Zoran Sucur, Free-Energy Surface Prediction by Flying Gaussian Method, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0032/v1.
Molecular simulations are computationally expensive, especially in systems with multiple free energy minima. To address this problem many enhanced sampling techniques have been developed. Metadynamics uses a bias potential defined as a sum of Gaussian hills in space of few (one or two) collective variables. This bias potential disfavors states that have been visited since the beginning of the simulation. Multiple walker metadynamics simulates the system in multiple parallel replicas. The bias potential disfavors states that have been visited since the beginning of the simulation in any replica. Flying Gaussian method presented here also simulates the system in multiple parallel replicas. It disfavors states that are, at certain moment, similar in two or more replicas. It was demonstrated on Alanine Dipeptide in vacuum and water, cis/trans-isomerisation of Proline-containing peptides and Met-enkephalin.
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|25.2 MiB||Input files (input structures, topology, Plumed input files) for simulations used to demonstrate functionality of Flying Gaussian algorithm (J. Chem. Theory Comput. 2016, 12, 4644-4650). The method simulates a molecular system in multiple replicas and enhances sampling by disfavoring states that are close to each other in different replicas. Input files for Flying Gaussian simulation of alanine dipeptide in vacuum and water, cis/trans isomerization of Ace-(Pro)n-NH2 and Met-enkephalin are provided.|
|2.5 KiB||README.txt file with descriptions.|
18 June 2019 [This version]