ENCORE Ensemble Similarity Calculations --- :mod:`MDAnalysis.analysis.encore`
| Author: | Matteo Tiberti, Wouter Boomsma, Tone Bengtsen |
|---|---|
| Year: | 2015-2017 |
| Copyright: | GNU Public License v3 |
| Maintainer: | Matteo Tiberti <matteo.tiberti@gmail.com>, mtiberti on github |
.. versionadded:: 0.16.0
The module contains implementations of similarity measures between protein ensembles described in [Lindorff-Larsen2009a]. The implementation and examples are described in [Tiberti2015a].
The module includes facilities for handling ensembles and trajectories through the :class:`Universe` class, performing clustering or dimensionality reduction of the ensemble space, estimating multivariate probability distributions from the input data, and more. ENCORE can be used to compare experimental and simulation-derived ensembles, as well as estimate the convergence of trajectories from time-dependent simulations.
ENCORE includes three different methods for calculations of similarity measures between ensembles implemented in individual functions:
- Harmonic Ensemble Similarity : :func:`~MDAnalysis.analysis.encore.similarity.hes`
- Clustering Ensemble Similarity : :func:`~MDAnalysis.analysis.encore.similarity.ces`
- Dimensional Reduction Ensemble Similarity : :func:`~MDAnalysis.analysis.encore.similarity.dres`
as well as two methods to evaluate the convergence of trajectories:
- Clustering based convergence evaluation : :func:`~MDAnalysis.analysis.encore.similarity.ces_convergence`
- Dimensionality-reduction based convergence evaluation : :func:`~MDAnalysis.analysis.encore.similarity.dres_convergence`
When using this module in published work please cite [Tiberti2015a].
.. toctree:: :maxdepth: 1 ./encore/similarity ./encore/clustering ./encore/dimensionality_reduction ./encore/confdistmatrix ./encore/covariance ./encore/bootstrap ./encore/utils
| [Lindorff-Larsen2009a] | Similarity Measures for Protein Ensembles. Lindorff-Larsen, K. Ferkinghoff-Borg, J. PLoS ONE 2008, 4, e4203. |
| [Tiberti2015a] | (1, 2) ENCORE: Software for Quantitative Ensemble.. Comparison. Matteo Tiberti, Elena Papaleo, Tone Bengtsen, Wouter Boomsma, Kresten Lindorff- Larsen. PLoS Comput Biol. 2015, 11, e1004415 |