RNA CoSSMos 2.0: an improved searchable database of secondary structure motifs in RNA three-dimensional structures

R3DMCS: a web server for visualizing structural variation in RNA motifs across experimental 3D structures from the same organism or across species

MOTIVATION

The recent progress in RNA structure determination methods has resulted in a surge of newly solved RNA 3D structures. However, there is an absence of a user-friendly browser-based tool that can facilitate the comparison and visualization of RNA motifs across multiple 3D structures.

RESULTS

We introduce R3DMCS, a web server that allows users to compare selected RNA nucleotides across all 3D structures of a given molecule from a given species, or across all 3D structures mapped to a single Rfam family. Starting from one instance of the motif, R3DMCS retrieves, aligns, annotates, organizes, and displays 3D coordinates of corresponding sets of nucleotides from other 3D structures. With R3DMCS, one can explore conformational changes of motifs due to 3D structures being solved in different functional states or different experimental conditions. One can also investigate conservation of 3D structure across species, or changes in 3D structure due to changes in sequence.

AVAILABILITY AND IMPLEMENTATION

R3DMCS is open-source software and freely available at https://rna.bgsu.edu/correspondence/ and https://github.com/BGSU-RNA/RNA-3D-correspondence.

The 5'-terminal stem-loop RNA element of SARS-CoV-2 features highly dynamic structural elements that are sensitive to differences in cellular pH

We present the nuclear magnetic resonance spectroscopy (NMR) solution structure of the 5'-terminal stem loop 5_SL1 (SL1) of the SARS-CoV-2 genome. SL1 contains two A-form helical elements and two regions with non-canonical structure, namely an apical pyrimidine-rich loop and an asymmetric internal loop with one and two nucleotides at the 5'- and 3'-terminal part of the sequence, respectively. The conformational ensemble representing the averaged solution structure of SL1 was validated using NMR residual dipolar coupling (RDC) and small-angle X-ray scattering (SAXS) data. We show that the internal loop is the major binding site for fragments of low molecular weight. This internal loop of SL1 can be stabilized by an A12-C28 interaction that promotes the transient formation of an A+•C base pair. As a consequence, the pKa of the internal loop adenosine A12 is shifted to 5.8, compared to a pKa of 3.63 of free adenosine. Furthermore, applying a recently developed pH-differential mutational profiling (PD-MaP) approach, we not only recapitulated our NMR findings of SL1 but also unveiled multiple sites potentially sensitive to pH across the 5'-UTR of SARS-CoV-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.

Identification and characterization of RNA pentaloop sequence families

One of the current methods for predicting RNA tertiary structure is fragment-based homology, which predicts tertiary structure from secondary structure. For a successful prediction, this method requires a library of the tertiary structures of small motifs clipped from previously solved RNA 3D structures. Because of the limited number of available tertiary structures, it is not practical to find structures for all sequences of all motifs. Identifying sequence families for motifs can fill the gaps because all sequences within a family are expected to have similar structural features. Currently, a collection of well-characterized sequence families has been identified for tetraloops. Because of their prevalence and biological functions, pentaloop structures should also be well-characterized. In this study, 10 pentaloop sequence families are identified. For each family, the common and distinguishing structural features are highlighted. These sequence families can be used to predict the tertiary structure of pentaloop sequences for which a solved structure is not available.

ProtNAff: protein-bound Nucleic Acid filters and fragment libraries

MOTIVATION

Atomistic models of nucleic acids (NA) fragments can be used to model the 3D structures of specific protein-NA interactions and address the problem of great NA flexibility, especially in their single-stranded regions. One way to obtain relevant NA fragments is to extract them from existing 3D structures corresponding to the targeted context (e.g. specific 2D structures, protein families, sequences) and to learn from them. Several databases exist for specific NA 3D motifs, especially in RNA, but none can handle the variety of possible contexts.

RESULTS

This article presents protNAff (protein-bound Nucleic Acids filters and fragments), a new pipeline for the conception of searchable databases on the 2D and 3D structures of protein-bound NA, the selection of context-specific (regions of) NA structures by combinations of filters, and the creation of context-specific NA fragment libraries. The strength of this pipeline is its modularity, allowing users to adapt it to many specific modeling problems. As examples, the pipeline is applied to the quantitative analysis of (i) the sequence-specificity of trinucleotide conformations, (ii) the conformational diversity of RNA at several levels of resolution, (iii) the effect of protein binding on RNA local conformations and (iv) the protein-binding propensity of RNA hairpin loops of various lengths.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available for download at URL https://github.com/isaureCdB/protNAff. The database and the trinucleotide fragment library are downloadable at URL https://zenodo.org/record/6483823#.YmbVhFxByV4.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Identification and Characterization of New RNA Tetraloop Sequence Families

There is an abundance of RNA sequence information available due to the efforts of sequencing projects. However, current techniques implemented to solve the tertiary structures of RNA, such as NMR and X-ray crystallography, are difficult and time-consuming. Therefore, biophysical techniques are not able to keep pace with the abundance of sequence information available. Because of this, there is a need to develop quick and efficient ways to predict RNA tertiary structure from sequence. One promising approach is to identify structural patterns within previously solved 3D structures and apply these patterns to new sequences. RNA tetraloops are one of the most common naturally occurring secondary structure motifs. Here, we use RNA Characterization of Secondary Structure Motifs (CoSSMos), Dissecting the Spatial Structure of RNA (DSSR), and a bioinformatic approach to search for and characterize tertiary structure patterns among tetraloops. Not surprising, we identified the well-known GNRA and UNCG tetraloops, as well as the previously identified RNYA tetraloop. However, some previously identified characteristics of these families were not observed in this data set, and some new characteristics were identified. In addition, we also identified and characterized three new tetraloop sequence families: YGAR, UGGU, and RMSA. This new structural information sheds light on the tertiary structure of tetraloops and contributes to the efforts of RNA tertiary structure prediction from sequence.