Modeling and Predicting RNA Three-Dimensional Structures

Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis-Westhof extended base pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. We show how to take advantage of this knowledge to quickly predict three-dimensional structures of large RNA molecules and present the RNA-MoIP web server (http://rnamoip.cs.mcgill.ca) that streamlines the computational and visualization processes. Finally, we show recent advances in the prediction of local 3D motifs from sequence data with the BayesPairing software and discuss its impact toward complete 3D structure prediction.