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PyEMMA specifications

Information


Unique identifier OMICS_15117
Name PyEMMA
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, Python
License GNU Lesser General Public License version 3.0
Computer skills Medium
Version 2.3
Stability Stable
Requirements
C/C++ compiler, setuptools, cython, numpy, scipy, matplotlib
Maintained Yes

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Versioning


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Documentation


Maintainer


  • person_outline Frank Noé

Publication for PyEMMA

PyEMMA citations

 (18)
library_books

Molecular details of dimerization kinetics reveal negligible populations of transient µ opioid receptor homodimers at physiological concentrations

2018
Sci Rep
PMCID: 5955887
PMID: 29769636
DOI: 10.1038/s41598-018-26070-8

[…] nected clusters, it cannot transition into another one without first passing through the unbound cluster. K-means clustering, transition matrix estimation and spectral clustering were performed using pyemma (version 2.3) python functions. […]

library_books

Examining a Thermodynamic Order Parameter of Protein Folding

2018
Sci Rep
PMCID: 5940758
PMID: 29740018
DOI: 10.1038/s41598-018-25406-8

[…] We employed PyEMMA 2 for carrying out the steps in the MSM construction. The raw Cartesian coordinates in the simulation trajectory of HP35, taken with a 1 ns interval here, were first represented by the cosines […]

library_books

Live Observation of Two Parallel Membrane Degradation Pathways at Axon Terminals

2018
PMCID: 5944365
PMID: 29551411
DOI: 10.1016/j.cub.2018.02.032

[…] on this volume [, ]. Areas with high correlation were traced to extract the compartment trajectories. To analyze the trajectories from the image analysis, a Markov state model (MSM) was defined with pyEMMA 2.4 [], where each state represents an area of 8×8 pixels (600 states in a 60 by 10 grid). The state assignment for each compartment was computed with the negative exponential distance to each […]

library_books

DNA sliding in nucleosomes via twist defect propagation revealed by molecular simulations

2018
Nucleic Acids Res
PMCID: 5887990
PMID: 29506273
DOI: 10.1093/nar/gky158

[…] rajectories reveals that these are the slowest-changing DNA contacts and are therefore sufficient to produce a minimal model of 601 repositioning.Markov state models were generated using the software PyEMMA 2 (). For each periodic sequence we produced a Bayesian MSM using 24, 24, 36, 36, 12 and 12 independent trajectories (107 MD steps each) and a lag time of 0.04, 0.1, 0.1, 0.02, 2 and 2 × 106 ti […]

library_books

Structural and biochemical characterization of the biuret hydrolase (BiuH) from the cyanuric acid catabolism pathway of Rhizobium leguminasorum bv. viciae 3841

2018
PLoS One
PMCID: 5806882
PMID: 29425231
DOI: 10.1371/journal.pone.0192736

[…] ed to further analysis via conformational clustering using the k-means algorithm with k = 20. These states were then assembled into a Markovian state model with a lag time of 40 nanoseconds using the PyEMMA python library []. The Caver algorithm [,] was used to identify a number of transiently forming tunnels linking the active sites of each monomer to the surface as well as the central cavity of […]

library_books

A scalable approach to the computation of invariant measures for high dimensional Markovian systems

2018
Sci Rep
PMCID: 5789124
PMID: 29379123
DOI: 10.1038/s41598-018-19863-4

[…] nent Analysis (TICA, lag time 50 nanoseconds),, this space was subsequently clustered into 200 disjoint states using K-means clustering. MD data processing and analysis was performed using MDTraj and PyEMMA,.Inspection of the obtained discrete time series {X(1),X(2),…,X(S)} reveals that it arrives in a set of terminal states which are not reversibly connected to the other Markov states. This is a […]


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PyEMMA institution(s)
Department for Mathematics and Computer Science, Freie Universitat, Berlin, Germany
PyEMMA funding source(s)
This work was supported by ERC starting grant pcCell, FU Berlin startup funds, Deutsche Forschungsgemeinschaft Grant Nos. 825/3-1 and SFB 1114, and Einstein Foundation Berlin, SoOPiC.

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