The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships

Abstract

AbstractSummaryThe DynaSig-ML (“Dynamical Signatures - Machine Learning”) Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. The DynaSig-ML package is built around the Elastic Network Contact Model (ENCoM), the first and only sequence-sensitive coarse-grained NMA model, which is used to generate the input Dynamical Signatures. Starting from in silico mutated structures, the whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps can also easily be parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the evolutionary fitness of the bacterial enzyme VIM-2 lactamase from deep mutational scan data.Availability and implementationDynaSig-ML is open source software available at https://github.com/gregorpatof/dynasigml_packageContactrafael.najmanovich@umontreal.ca