Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation

The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput experimental maturation efficiency data of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available.Author summary: Ribonucleic acids (RNAs) are biomolecules which play essential roles in the function of all living organisms. These molecules can adopt defined 3D structures in the cell, but they also move around their equilibrium structure. RNA function is intimately related to structural dynamics, which, however, can be costly to simulate. In the present study, we adapt a fast method for the computational study of protein dynamics, called ENCoM, to work on RNA molecules. We benchmark its performance against other similar methods and find that ENCoM has a clear advantage when it comes to predicting large-scale dynamics. Moreover, ENCoM is unique in its ability to predict the effect of mutations on structural dynamics, as was already shown for proteins. This ability extends to RNA: we capture dynamics-function relationships apparent from experimental maturation efficiency data on over 26 000 sequence variants of a human microRNA, miR-125a. These dynamics-function relationships are learned by a novel linear model combining the reduced ENCoM dynamical information and the energy of folding. The low computational cost of this technique opens up the possibility of high-throughput prediction of RNA and protein functional properties from sequence information, if starting structures are known or can be predicted.