Open AccessHigh-Performance Data Analysis on the Big Trajectory Data of Cellular Scale All-atom Molecular Dynamics Simulations
Author(s) -
Isseki Yu,
Michael Feig,
Yuji Sugita
Publication year - 2018
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1036/1/012009
Subject(s) - molecular dynamics , trajectory , biomolecule , big data , scale (ratio) , atom (system on chip) , macromolecule , computer science , statistical physics , physics , biological system , nanotechnology , chemistry , computational chemistry , materials science , biology , data mining , quantum mechanics , parallel computing , biochemistry
The inside of a cell is highly crowded with a large number of macromolecules together with solvents and metabolites. To know the molecular-level behaviour of biomolecules in such dense crowding environment, we constructed full atomistic model of the cytoplasm of bacteria, and performed massive all-atom molecular dynamics (MD) simulations. On the other hand, to analyse such big MD data, we need significant computational power and efficient calculation methodology. Here, we introduce what and how we analyse the biomolecule properties from the big trajectory data produced by cellular scale all-atom MD simulations.