A Nucleobase-Centric Coarse-Grained Model for Structure Prediction of RNA Fragments

The structural characterization of RNA usually poses additional challenges when compared to other biomolecular systems. For example, there is a relatively scarce amount of structural data available, which demands the development of three-dimensional structure prediction tools. In addition, full-resolution simulations can be a hard task not only because of the complexity of the interactions involved, but also due to the limitations of the current force fields. At this point, coarse-grained simulations are a good candidate to fill the gaps in this growing research field. The use of these techniques has rapidly increased in the past decades in the study of biological systems on which the experiments require an additional interpretation or where an atomistic computational approach results difficult or unfeasible. Nevertheless, the development of coarse-grained models involves the understanding of the main structural features of the original system, which can represent a challenge by itself. In this work, we present a knowledge-base coarse-grained model for RNA structure prediction, representing each nucleotide by a single anisotropic particle. The mapping and the main interactions are designed to reproduce the geometrical distribution of the closest pairs of nucleotides, extracted from a set of ribosomal structures. The model is inspired in the ESCORE function [1], a knowledge-based scoring function that has been shown to perform better compared to fully atomistic techniques in identifying native-like structures from a set of decoys. Its minimalistic nature and successful application on a broad range of structures straightforwardly suggest a representation for a coarse-grained approach. We show the preliminary results of our simulations and discuss the role of pair interactions in the prediction of RNA structures. [1] S. Bottaro, F. Di Palma and G. Bussi, “The Role of Nucleobase Interactions in RNA Structure and Dynamics”, Nucleic Acids Res., accepted.