A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs

TL;DR

The paper presents SPQR, a coarse-grained RNA model that accurately predicts RNA structures, including complex motifs, by representing nucleotides with specialized particles and knowledge-based interactions, improving structure prediction accuracy.

Contribution

Introduction of the SPQR coarse-grained model for RNA structure prediction, capable of capturing diverse base interactions and conformations, with a method to reintroduce atomistic detail.

Findings

Successfully predicts structures of various RNA motifs with correct contacts

Distinguishes between different base pairing and conformations

Effective in modeling complex RNA structures like pseudoknots

Abstract

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the ESCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-base model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the coarse-grained representation.