  RNA is a fundamental class of biomolecules that mediate a large variety of
molecular processes within the cell. Computational algorithms can be of great
help in the understanding of RNA structure-function relationship. One of the
main challenges in this field is the development of structure-prediction
algorithms, which aim at the prediction of the three-dimensional (3D) native
fold from the sole knowledge of the sequence. In a recent paper, we have
introduced a scoring function for RNA structure prediction. Here, we analyze in
detail the performance of the method, we underline strengths and shortcomings,
and we discuss the results with respect to state-of-the-art techniques. These
observations provide a starting point for improving current methodologies, thus
paving the way to the advances of more accurate approaches for RNA 3D structure
prediction.
