iPARTS2: an improved tool for pairwise alignment of RNA tertiary structures, version 2

A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures

Understanding the 3D structure of RNA is key to understanding RNA function. RNA 3D structure is modular and can be seen as a composition of building blocks of various sizes called tertiary motifs. Currently, long-range motifs formed between distant loops and helical regions are largely less studied than the local motifs determined by the RNA secondary structure. We surveyed long-range tertiary interactions and motifs in a non-redundant set of non-coding RNA 3D structures. A new dataset of annotated LOng-RAnge RNA 3D modules (LORA) was built using an approach that does not rely on the automatic annotations of non-canonical interactions. An original algorithm, ARTEM, was developed for annotation-, sequence- and topology-independent superposition of two arbitrary RNA 3D modules. The proposed methods allowed us to identify and describe the most common long-range RNA tertiary motifs. Along with the prevalent canonical A-minor interactions, a large number of previously undescribed staple interactions were observed. The most frequent long-range motifs were found to belong to three main motif families: planar staples, tilted staples, and helical packing motifs.

CircularSTAR3D: a stack-based RNA 3D structural alignment tool for circular matching

The functions of non-coding RNAs usually depend on their 3D structures. Therefore, comparing RNA 3D structures is critical in analyzing their functions. We noticed an interesting phenomenon that two non-coding RNAs may share similar substructures when rotating their sequence order. To the best of our knowledge, no existing RNA 3D structural alignment tools can detect this type of matching. In this article, we defined the RNA 3D structure circular matching problem and developed a software tool named CircularSTAR3D to solve this problem. CircularSTAR3D first uses the conserved stacks (consecutive base pairs with similar 3D structures) in the input RNAs to identify the circular matched internal loops and multiloops. Then it performs a local extension iteratively to obtain the whole circular matched substructures. The computational experiments conducted on a non-redundant RNA structure dataset show that circular matching is ubiquitous. Furthermore, we demonstrated the utility of CircularSTAR3D by detecting the conserved substructures missed by regular alignment tools, including structural motifs and conserved structures between riboswitches and ribozymes from different classes. We anticipate CircularSTAR3D to be a valuable supplement to the existing RNA 3D structural analysis techniques.

LocalSTAR3D: a local stack-based RNA 3D structural alignment tool

A fast-growing number of non-coding RNA structures have been resolved and deposited in Protein Data Bank (PDB). In contrast to the wide range of global alignment and motif search tools, there is still a lack of local alignment tools. Among all the global alignment tools for RNA 3D structures, STAR3D has become a valuable tool for its unprecedented speed and accuracy. STAR3D compares the 3D structures of RNA molecules using consecutive base-pairs (stacks) as anchors and generates an optimal global alignment. In this article, we developed a local RNA 3D structural alignment tool, named LocalSTAR3D, which was extended from STAR3D and designed to report multiple local alignments between two RNAs. The benchmarking results show that LocalSTAR3D has better accuracy and coverage than other local alignment tools. Furthermore, the utility of this tool has been demonstrated by rediscovering kink-turn motif instances, conserved domains in group II intron RNAs, and the tRNA mimicry of IRES RNAs.

RMalign: an RNA structural alignment tool based on a novel scoring function RMscore

Background

RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases.

Results

Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.88 and 0.78, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.

Conclusion

Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5631-3) contains supplementary material, which is available to authorized users.