The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes

TL;DR

NRGTEN is an extensible Python toolkit that implements multiple coarse-grained normal mode analysis models, including the novel ENCoM model, enabling advanced biomolecular dynamics studies and mutation effect predictions.

Contribution

The paper introduces NRGTEN, a flexible Python package that incorporates new and existing NMA models, notably ENCoM, for enhanced biomolecular analysis and mutation impact prediction.

Findings

Includes the novel ENCoM model accounting for atomic interactions.

Enables prediction of mutation effects on stability and conformational transitions.

Provides tools for generating conformational ensembles for docking studies.

Abstract

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