  RNA molecules are essential cellular machines performing a wide variety of
functions for which a specific three-dimensional structure is required. Over
the last several years, experimental determination of RNA structures through
X-ray crystallography and NMR seems to have reached a plateau in the number of
structures resolved each year, but as more and more RNA sequences are being
discovered, need for structure prediction tools to complement experimental data
is strong. Theoretical approaches to RNA folding have been developed since the
late nineties when the first algorithms for secondary structure prediction
appeared. Over the last 10 years a number of prediction methods for 3D
structures have been developed, first based on bioinformatics and data-mining,
and more recently based on a coarse-grained physical representation of the
systems. In this review we are going to present the challenges of RNA structure
prediction and the main ideas behind bioinformatic approaches and physics-based
approaches. We will focus on the description of the more recent physics-based
phenomenological models and on how they are built to include the specificity of
the interactions of RNA bases, whose role is critical in folding. Through
examples from different models, we will point out the strengths of
physics-based approaches, which are able not only to predict equilibrium
structures, but also to investigate dynamical and thermodynamical behavior, and
the open challenges to include more key interactions ruling RNA folding.
