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

Abstract

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.

ARTEMIS - a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment

“…To identify the optimal alignment, we developed the ARTEMIS algorithm (using ARTEM [20] to I nfer S equence alignment), which operates under the assumption that the ideal superposition involves at least one pair of matched residues exhibiting a near-zero RMSD.…”

“…ARTEMIS superimposes the query structure Y on the reference X across all possible residue pairs between the structures (Supplementary Figure S1, lines [20][21][22][23][24][25][26][27][28][29][30][31][32]. Each superposition is computed using the Kabsch algorithm [21], employing a 3-atom representation of the residues.…”

“…The TM1-Score RNA and TM2-Score RNA yielding the maximum sum are then assigned to the candidate alignment. Similar to ARTEM [20], the theoretical time complexity of ARTEMIS is O(N 2 * M 2 ), given that ARTEMIS computes N*M pairwise distances to determine the set of mutually closest residues for each of the N*M single-residue seed superpositions (Supplementary Figure S1, lines 20-30).…”

“…In this work, we present ARTEMIS, an application of ARTEM methodology to address the global RNA 3D structure alignment problem. The heuristic behind ARTEMIS operates in polynomial time and ensures the optimal solution, provided it includes at least one residue-residue match with a near-zero RMSD, a condition commonly met in RNA structures due to their characteristic recurrent interactions [20]. ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition.…”

SQUARNA - an RNA secondary structure prediction method based on a greedy stem formation model

“…The SQUARNA approach is rooted in the ARTEM algorithm, designed for the superposition of two arbitrary RNA 3D structure fragments without prior knowledge of nucleotide matchings between the fragments [42]. The two core ideas of ARTEM are the formulation of the problem as the partial assignment problem and the utilization of dependencies in the data.…”