deltaGseg: macrostate estimation via molecular dynamics simulations and multiscale time series analysis
Author(s) -
Diana Low,
Efthymios Motakis
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt413
Subject(s) - bioconductor , decorrelation , computer science , series (stratigraphy) , molecular dynamics , r package , statistical physics , algorithm , cluster analysis , biological system , data mining , artificial intelligence , computational science , biology , computational chemistry , physics , chemistry , paleontology , biochemistry , gene
Binding free energy calculations obtained through molecular dynamics simulations reflect intermolecular interaction states through a series of independent snapshots. Typically, the free energies of multiple simulated series (each with slightly different starting conditions) need to be estimated. Previous approaches carry out this task by moving averages at certain decorrelation times, assuming that the system comes from a single conformation description of binding events. Here, we discuss a more general approach that uses statistical modeling, wavelets denoising and hierarchical clustering to estimate the significance of multiple statistically distinct subpopulations, reflecting potential macrostates of the system. We present the deltaGseg R package that performs macrostate estimation from multiple replicated series and allows molecular biologists/chemists to gain physical insight into the molecular details that are not easily accessible by experimental techniques.
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