Dynamical Network Analysis

The Python implementation of Dynamical Network Analysis was built to provide all functionalities necessary to the analysis of Molecular Dynamics (MD) simulations using the Dynamical Network Analysis method.

Install

Install the latest version of Dynamical Network Analysis::

$ pip install dynetan

For additional details, please see INSTALL.md.

Tutorial

For an introduction to this implementation, a jupyter notebook tutorial has been prepared and released along with its latest publication:

• Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories. J. Chem. Phys. (2020). DOI: 10.1063/5.0018980

Documentation

A full documentation can be found at https://dynamical-network-analysis.readthedocs.io/en/latest/index.html

Resources and References

For the background on Dynamical Network Analysis, see the following papers that describe its development:

• Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories. J. Chem. Phys. (2020). DOI: 10.1063/5.0018980

• Experimental and computational determination of tRNA dynamics. FEBS Letters (2010). DOI: 10.1016/j.febslet.2009.11.061

• Exit strategies for charged tRNA from GluRS. JMB (2010). DOI: 10.1016/j.jmb.2010.02.003

• Dynamical Networks in tRNA:protein complexes. PNAS (2009). DOI: 10.1073/pnas.0810961106