Vfold: a web server for RNA structure and folding thermodynamics prediction

Abstract

Background: The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies.

Results: The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization.

Conclusions: The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at "http://rna.physics.missouri.edu".

Figure 1. RNA 3D template structure database.

Figure 2. An example of Vfold2D prediction: the input information highlighted in the snapshot of the Vfold2D web server are the sequence (32 nts in this example), the temperature (25°C), the energy parameters used for base stacks (from MFOLD in this example) the structural type (non-pseudoknotted in this example).

(1) Vfold2D gives a list of base pair probabilities P_ij (in txt format) between nucleotides i and j. For example, the probability of forming G1-C12 base pair is 0.22. (2) The most probable 2D structure is derived from the base pairs with P_ij>0.5. In this example, the predicted most probable 2D structure (plotted by VARNA in the figure) has the probability of 0.78. (3) Vfold2D also predicts all the possible helices from the predicted base pair probabilities. (4) Possible alternative structures can be found from the helix and base pair probabilities ( in this example).

Figure 3. An example of the VfoldThermal prediction: the inputs highlighted in the snapshot of VfoldThermal web server are the sequence (32 nts in this example) with the temperature range of 0°C–100°C, the energy parameters used for base stacks (from MFOLD in this example) and the structure type (non-pseudoknotted in this example).

From the temperature dependence of the partition function Q(T), VfoldThermal gives a list of temperature-dependent heat capacity C(T), with temperature interval of 0.5°C. The eps format of melting curve is generated by Gnuplot.

Figure 4. An example of the Vfold3D prediction: the snapshot of Vfold3D web server highlights the input sequence (32 nts for this example) and the 2D structures as defined by the base pairs.

(a) For the most probable 2D structure shown in Fig. 2, Vfold3D predicts 3D structure based on the templates from the known structures. (b) For the predicted alternative structure shown in Fig. 2, Vfold3D cannot predict the 3D structure due to the lack of the available template for the single-stranded chain between the helices.