  Summary: Coarse-grained normal mode analysis (NMA) is a fast computational
technique to study the dynamics of biomolecules. Here we present the
Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN). NRGTEN is a
Python toolkit that implements four different NMA models in addition to popular
and novel metrics to benchmark and measure properties from these models.
Furthermore, the toolkit is available as a public Python package and is easily
extensible for the development or implementation of additional NMA models. The
inclusion of the ENCoM model (Elastic Network Contact Model) developed in our
group within NRGTEN is noteworthy, owing to its account for the specific
chemical nature of atomic interactions. This makes possible some unique
predictions of the effect of mutations, such as on stability (via changes in
vibrational entropy differences), on the transition probability between
different conformational states or on the flexibility profile of the whole
macromolecule/complex (to study allostery and signalling). In addition, all NMA
models can be used to generate conformational ensembles from a starting
structure to aid in protein-protein, protein-ligand or other docking studies
among applications. NRGTEN is freely available via a public Python package
which can be easily installed on any modern machine and includes a detailed
user guide hosted online. Availability and implementation:
https://github.com/gregorpatof/nrgten_package/ Contact:
rafael.najmanovich@umontreal.ca
