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Summary: The DynaSig-ML (Dynamical Signatures - Machine Learning) Pythonpackage allows the efficient, user-friendly exploration of 3D dynamics-functionrelationships in biomolecules, using datasets of experimental measures fromlarge numbers of sequence variants. The DynaSig-ML package is built around theElastic Network Contact Model (ENCoM), the first and only sequence-sensitivecoarse-grained NMA model, which is used to generate the input DynamicalSignatures. Starting from in silico mutated structures, the whole pipeline canbe run with just a few lines of Python and modest computational resources. Thecompute-intensive steps can also easily be parallelized in the case of eitherlarge biomolecules or vast amounts of sequence variants. As an exampleapplication, we use the DynaSig-ML package to predict the evolutionary fitnessof the bacterial enzyme VIM-2 lactamase from deep mutational scan data.Availability and implementation: DynaSig-ML is open source software availableat https: //github.com/gregorpatof/dynasigml package Contact:rafael.najmanovich@umontreal.ca

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