Over the past two decades, it has become increasingly clear that ribonucleic acid (RNA) participates in numerous cellular functions in addition to storing and relaying genetic information. This participation is primarily due to its capability to adopt complex and diverse three-dimensional folds that can recognize different proteins and small molecules, thereby catalyzing a wide range of biochemical reactions in cells. Such tightly regulated reactions include the synthesis of proteins and transcriptional and translational regulation. Although the conformational landscape accessible to RNAs is limited when compared to proteins, RNAs form various 3D folds featuring many structural motifs, e.g., A-form helices, stem-loops, internal loops, bulges, multi-branched loops, pseudoknots, G-quadruplexes, and single-stranded regions, to name a few. Among these structural motifs, pseudoknots are the most intriguing ones, found in almost all classes of RNAs.

Pseudoknots, defined as structural motifs resulting from the base pairing between a single-stranded region and a loop of a hairpin, have now been established as a widespread motif with diverse biological functions. Depending on the interacting regions and geometries, pseudoknots have been largely classified into H-, K-, L-, and M-type, with H-type being the most prevalent in different RNAs. Because of various possible topologies that a pseudoknot can adopt, it is quite challenging to predict if a given sequence will form a pseudoknot, what type of pseudoknot it will form, and whether it will be stable in a given ionic condition. One can use standard structure determination techniques, including X-ray crystallography, NMRspectroscopy, and cryogenic electron microscopy; however, the cost involved with such measurements is too high, and they are not high-throughput approaches. Thus, a battery of theoretical and computational methods (e.g., FARNA, Vfold3D, RNAComposer, 3dRNA, FebRNA, iFold, SimRNA, HiRE-RNA, RACER, and IsRNA) have been developed to address the issue. Although these models can make good predictions of 3D structures of small- and medium-length RNAs, making quantitative predictions for conformational stability for the RNAs still remains a challenge. A few models have also been proposed to predict the thermal stabilities of RNAs, e.g., Vfold2D/VfoldThermal, three interaction sites, oxRNA, and iFold. However, it is important to note that the RNA conformational stability depends strongly on the ionic conditions of the solution, wherein the effect of ions on the stability is rarely considered as a parameter in these prediction models. Even though all-atom molecular simulations help capture the RNA-ion interactions, the technique is neither cost effective nor high throughput.

Zhi-Jie Tan and co-workers previously developed a coarse-grain (CG) model capable of predicting sequence-specific thermodynamic parameters in addition to making reliable predictions on the 3D structures and stabilities for relatively simple RNA conformations, including hairpins, double-stranded RNAs, kissing complexes, and H-type pseudoknots. In the study titled, “Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions” published in the April 18 issue of Biophysical Journal, Zhi-Jie Tan and co-workers improvised the previously developed CG model by incorporating a new CG force field and a replica-exchange Monte-Carlo algorithm. The force field was reparameterized against a larger experimental Protein Data Bank data set containing a large number of complex RNAs of multi-way junctions. A new scoring function was used to identify top-scored structures from the predicted conformational pool. With the added features, the proposed CG model can now reliably predict the near-native 3D structures and quantify the conformational stabilities of complex RNA pseudoknots beyond the minimal H-type pseudoknot in monovalent and bivalent ion solutions. The authors could also analyze the thermal unfolding pathways for the complex RNA pseudoknots in different ion solutions. Because the knowledge of 3D structures and their conformational stabilities is extremely important for understanding the biological function of RNA pseudoknots, this new model provides a way to obtain structural information for different RNA sequences of varied lengths in the presence of monovalent/bivalent ions in a high-throughput manner by using only primary sequence information.