Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives

Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

rnaCrosslinkOO: an object-oriented R package for the analysis of RNA structural data generated by RNA crosslinking experiments

SUMMARY

RNA (ribonucleic acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localization. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analyzing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction.

AVAILABILITY AND IMPLEMENTATION

rnaCrosslinkOO is freely available to noncommercial users and implemented in R, with the source code and documentation accessible at https://CRAN.R-project.org/package=rnaCrosslinkOO. The software is supported on Linux, macOS, and Windows platforms.

Using 5' 32 P-labeled Primer and Reverse Transcription to Probe RNA Structure

RNA molecules perform numerous cellular functions necessary for cell viability, some of which can depend on the RNA's structure. Therefore, it is important to study RNA structure and RNA folding to better understand the molecular basis of these functions. RNA chemical mapping strategies to elucidate RNA structural changes involve using chemical reagents that form adducts or cleave RNA. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) measures RNA flexibility by modification of the 2' hydroxyl groups of flexible nucleotides. These RNA adducts can be detected using ³² P-labeled primers and reverse transcription (RT) followed by PAGE analysis. This strategy can reveal the base-paired regions of the RNA and provide insight into tertiary structure and solvent accessibility. This protocol provides a method to interrogate RNA structure using furoyl acylimidazole (FAI). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Reverse transcription (RT) primer labeling with ³² P radionuclide Basic Protocol 2: Characterization of RNA structure with radiolabeled primer and reverse transcription (RT).

Led-Seq: ligation-enhanced double-end sequence-based structure analysis of RNA

Structural analysis of RNA is an important and versatile tool to investigate the function of this type of molecules in the cell as well as in vitro. Several robust and reliable procedures are available, relying on chemical modification inducing RT stops or nucleotide misincorporations during reverse transcription. Others are based on cleavage reactions and RT stop signals. However, these methods address only one side of the RT stop or misincorporation position. Here, we describe Led-Seq, a new approach based on lead-induced cleavage of unpaired RNA positions, where both resulting cleavage products are investigated. The RNA fragments carrying 2', 3'-cyclic phosphate or 5'-OH ends are selectively ligated to oligonucleotide adapters by specific RNA ligases. In a deep sequencing analysis, the cleavage sites are identified as ligation positions, avoiding possible false positive signals based on premature RT stops. With a benchmark set of transcripts in Escherichia coli, we show that Led-Seq is an improved and reliable approach based on metal ion-induced phosphodiester hydrolysis to investigate RNA structures in vivo.

Overview of Chemical Methods to Probe RNA Structure with Radionucleotides

Structural features of RNA play an important role in its capability to perform various functions in biological systems. To probe structural features, chemical probes are used to conjugate or cleave RNA at solvent-accessible sites, differentiating between flexible and constrained regions. These conjugates or cleaved products are then detected using reverse transcription (RT), where enzymatic RNA-dependent DNA primer extension is abruptly halted at the conjugation site or cleavage site. Here, we provide an overview of methods to probe RNA structure in vitro using radioactively labeled DNA primers, which provide a highly sensitive method to visualize RT stop sites with gel electrophoresis. © 2023 Wiley Periodicals LLC.

How does precursor RNA structure influence RNA processing and gene expression?

RNA is a fundamental biomolecule that has many purposes within cells. Due to its single-stranded and flexible nature, RNA naturally folds into complex and dynamic structures. Recent technological and computational advances have produced an explosion of RNA structural data. Many RNA structures have regulatory and functional properties. Studying the structure of nascent RNAs is particularly challenging due to their low abundance and long length, but their structures are important because they can influence RNA processing. Precursor RNA processing is a nexus of pathways that determines mature isoform composition and that controls gene expression. In this review, we examine what is known about human nascent RNA structure and the influence of RNA structure on processing of precursor RNAs. These known structures provide examples of how other nascent RNAs may be structured and show how novel RNA structures may influence RNA processing including splicing and polyadenylation. RNA structures can be targeted therapeutically to treat disease.

Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism

Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem-loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling

MOTIVATION

Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.

RESULTS

Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction.

AVAILABILITY AND IMPLEMENTATION

SPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-throughput mapping of RNA solvent accessibility at the single-nucleotide resolution by RtcB ligation between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage

Given the challenges for the experimental determination of RNA tertiary structures, probing solvent accessibility has become increasingly important to gain functional insights. Among various chemical probes developed, backbone-cleaving hydroxyl radical is the only one that can provide unbiased detection of all accessible nucleotides. However, the readouts have been based on reverse transcription (RT) stop at the cleaving sites, which are prone to false positives due to PCR amplification bias, early drop-off of reverse transcriptase, and the use of random primers in RT reaction. Here, we introduced a fixed-primer method called RL-Seq by performing RtcB Ligation (RL) between a fixed 5'-OH-end linker and unique 3'-P-end fragments from hydroxyl radical cleavage prior to high-throughput sequencing. The application of this method to E. coli ribosomes confirmed its ability to accurately probe solvent accessibility with high sensitivity (low required sequencing depth) and accuracy (strong correlation to structure-derived values) at the single-nucleotide resolution. Moreover, a near-perfect correlation was found between the experiments with and without using unique molecular identifiers, indicating negligible PCR biases in RL-Seq. Further improvement of RL-Seq and its potential transcriptome-wide applications are discussed.