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

Abstract

Abstract   The 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. It does so by predicting 3D structural dynamics for every variant using the Elastic Network Contact Model (ENCoM), a sequence-sensitive coarse-grained normal mode analysis model. Dynamical Signatures represent the fluctuation at every position in the biomolecule and are used as features fed into machine learning models of the user’s choice. Once trained, these models can be used to predict experimental outcomes for theoretical variants. The whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps are easily 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 maturation efficiency of human microRNA miR-125a variants from high-throughput enzymatic assays. Availability and implementation DynaSig-ML is open-source software available at https://github.com/gregorpatof/dynasigml_package.