RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data

Canada's contributions to RNA research: past, present, and future perspectives

The field of RNA research has provided profound insights into the basic mechanisms modulating the function and adaption of biological systems. RNA has also been at the center stage in the development of transformative biotechnological and medical applications, perhaps most notably was the advent of mRNA vaccines that were critical in helping humanity through the Covid-19 pandemic. Unbeknownst to many, Canada boasts a diverse community of RNA scientists, spanning multiple disciplines and locations, whose cutting-edge research has established a rich track record of contributions across various aspects of RNA science over many decades. Through this position paper, we seek to highlight key contributions made by Canadian investigators to the RNA field, via both thematic and historical viewpoints. We also discuss initiatives underway to organize and enhance the impact of the Canadian RNA research community, particularly focusing on the creation of the not-for-profit organization RNA Canada ARN. Considering the strategic importance of RNA research in biology and medicine, and its considerable potential to help address major challenges facing humanity, sustained support of this sector will be critical to help Canadian scientists play key roles in the ongoing RNA revolution and the many benefits this could bring about to Canada.

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework

MOTIVATION

The prediction of RNA structure canonical base pairs from a single sequence, especially pseudoknotted ones, remains challenging in a thermodynamic models that approximates the energy of the local 3D motifs joining canonical stems. It has become more and more apparent in recent years that the structural motifs in the loops, composed of noncanonical interactions, are essential for the final shape of the molecule enabling its multiple functions. Our capacity to predict accurate 3D structures is also limited when it comes to the organization of the large intricate network of interactions that form inside those loops.

RESULTS

We previously developed the integer programming framework RNA Motifs over Integer Programming (RNAMoIP) to reconcile RNA secondary structure and local 3D motif information available in databases. We further develop our model to now simultaneously predict the canonical base pairs (with pseudoknots) from base pair probability matrices with or without alignment. We benchmarked our new method over the all nonredundant RNAs below 150 nucleotides. We show that the joined prediction of canonical base pairs structure and local conserved motifs (i) improves the ratio of well-predicted interactions in the secondary structure, (ii) predicts well canonical and Wobble pairs at the location where motifs are inserted, (iii) is greatly improved with evolutionary information, and (iv) noncanonical motifs at kink-turn locations.

AVAILABILITY AND IMPLEMENTATION

The source code of the framework is available at https://gitlab.info.uqam.ca/cbe/RNAMoIP and an interactive web server at https://rnamoip.cbe.uqam.ca/.

MicroRNAs Regulate Tumorigenesis by Downregulating SOCS3 Expression: An In silico Approach

Tumor microenvironment is characterized by the occurrence of significant changes due to disrupted signaling pathways that affect a broad spectrum of cellular activities such as proliferation, differentiation, signaling, invasiveness, migration, and apoptosis. Similarly, a downregulated suppressor of cytokine signaling 3 (SOCS3) promotes increased JAK/STAT function due to aberrant cytokine signaling, which results in increased cell proliferation, differentiation, and migration. Multiple carcinomas including breast cancer, prostate cancer, hepatocellular carcinoma, pancreatic cancer, and colorectal cancer involve the disruption of SOCS3 expression due to microRNA overexpression. MicroRNAs are small, conserved, and non-coding RNA molecules that regulate gene expression through post-transcriptional inhibition and mRNA destabilization. The aim of this study was to identify putative microRNAs that interact with SOCS3 and downregulate its expression. In this study, miRWalk, TargetScan, and miRDB were used to identify microRNAs that interact with SOCS3, whereas RNA22 was utilized to identify the binding sites of 238 significant microRNAs. The tertiary structures of shortlisted microRNAs and SOCS3 regions were predicted through MC Sym and RNAComposer, respectively. For molecular docking, HDOCK was used, which predicted 80 microRNA-messengerRNA complexes and the interactions of the top 5 shortlisted complexes were assessed. The complexes were shortlisted on the basis of least binding affinity score and maximum confidence score. This study identifies the interactions of known (miR-203a-5p) and novel (miR-6756-5p, miR-6732-5p, miR-1203, miR-6887-5p) microRNAs with SOCS3 regions due to their maximum interactions. Identifying the interactions of these microRNAs with SOCS3 will significantly advance the understanding of oncomiRs (miRNAs that are associated with cancer development) in tumor development due to their influence on SOCS3 expression. These insights will assist in future studies to understand the significance of miRNA-SOCS3-associated tumor development and progression.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

Exploring the Energy Landscape of Riboswitches Using Collective Variables Based on Tertiary Contacts

Messenger RNA regulatory elements, such as riboswitches, can display a high degree of flexibility. By characterizing their energy landscapes, and corresponding distributions of 3D configurations, structure-function relationships can be elucidated. Molecular dynamics simulation with enhanced sampling is an important strategy used to computationally access free energy landscapes characterizing the accessible 3D conformations of RNAs. While tertiary contacts are thought to play important roles in RNA dynamics, it is difficult, in explicit solvent, to sample the formation and breakage of tertiary contacts, such as helix-helix interactions, pseudoknot interactions, and junction interactions, while maintaining intact secondary structure elements. To this end, we extend previously developed collective variables and metadynamics efforts, to establish a simple metadynamics protocol, which utilizes only one collective variable, based on multiple tertiary contacts, to characterize the underlying free energy landscape of any RNA molecule. We develop a modified collective variable, the tertiary contacts distance (Q_TC), which can probe the formation and breakage of all or selectively chosen tertiary contacts of the RNA. The SAM-I riboswitch in the presence of three ionic and substrate conditions was investigated and validated against the structure ensemble previously generated using SAXS experiments. This efficient and easy to implement all-atom MD simulation based approach incorporating metadynamics to study RNA conformational dynamics can also be transferred to any other type of biomolecule.

SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability

RNA secondary structures can carry out essential cellular functions alone or interact with one another to form the hierarchical tertiary structures. Experimental structure identification approa ches can show the in vitro structures of RNA molecules. However, they usually have limits in the resolution and are costly. In silico structure prediction tools are thus primarily relied on for pre-experiment analysis. Various structure prediction models have been developed over the decades. Since these tools are usually used before knowing the actual RNA structures, evaluating and ranking the pile of secondary structure predictions of a given sequence is essential in computational analysis. In this research, we implemented a web service called SSRTool (RNA Secondary Structure prediction Ranking Tool) to assist in the ranking and evaluation of the generated predicted structures of a given sequence. Based on the computed species-specific interpretability significance in four common RNA structure–function aspects, SSRTool provides three functions along with visualization interfaces: (1) Rank user-generated predictions. (2) Provide an automated streamline of structure prediction and ranking for a given sequence. (3) Infer the functional aspects of a given structure. We demonstrated the applicability of SSRTool via real case studies and reported the similar trends between computed species-specific rankings and the corresponding prediction F1 values. The SSRTool web service is available online at https://cobisHSS0.im.nuk.edu.tw/SSRTool/, http://cosbi3.ee.ncku.edu.tw/SSRTool/, or the redirecting site https://github.com/cobisLab/SSRTool/.

Designing strategies of small-molecule compounds for modulating non-coding RNAs in cancer therapy

Non-coding RNAs (ncRNAs) have been defined as a class of RNA molecules transcribed from the genome but not encoding proteins, such as microRNAs, long non-coding RNAs, Circular RNAs, and Piwi-interacting RNAs. Accumulating evidence has recently been revealing that ncRNAs become potential druggable targets for regulation of several small-molecule compounds, based on their complex spatial structures and biological functions in cancer therapy. Thus, in this review, we focus on summarizing some new emerging designing strategies, such as high-throughput screening approach, small-molecule microarray approach, structure-based designing approach, phenotypic screening approach, fragment-based designing approach, and pharmacological validation approach. Based on the above-mentioned approaches, a series of representative small-molecule compounds, including Bisphenol-A, Mitoxantrone and Enoxacin have been demonstrated to modulate or selectively target ncRNAs in different types of human cancers. Collectively, these inspiring findings would provide a clue on developing more novel avenues for pharmacological modulations of ncRNAs with small-molecule drugs for future cancer therapeutics.

On the predictibility of A-minor motifs from their local contexts

This study investigates the importance of the structural context in the formation of a type I/II A-minor motif. This very frequent structural motif has been shown to be important in the spatial folding of RNA molecules. We developed an automated method to classify A-minor motif occurrences according to their 3D context similarities, and we used a graph approach to represent both the structural A-minor motif occurrences and their classes at different scales. This approach leads us to uncover new subclasses of A-minor motif occurrences according to their local 3D similarities. The majority of classes are composed of homologous occurrences, but some of them are composed of non-homologous occurrences. The different classifications we obtain allow us to better understand the importance of the context in the formation of A-minor motifs. In a second step, we investigate how much knowledge of the context around an A-minor motif can help to infer its presence (and position). More specifically, we want to determine what kind of information, contained in the structural context, can be useful to characterize and predict A-minor motifs. We show that, for some A-minor motifs, the topology combined with a sequence signal is sufficient to predict the presence and the position of an A-minor motif occurrence. In most other cases, these signals are not sufficient for predicting the A-minor motif, however we show that they are good signals for this purpose. All the classification and prediction pipelines rely on automated processes, for which we describe the underlying algorithms and parameters.

Getting to the bottom of lncRNA mechanism: structure-function relationships

While long non-coding RNAs are known to play key roles in disease and development, relatively few structural studies have been performed for this important class of RNAs. Here, we review functional studies of long non-coding RNAs and expose the need for high-resolution 3-D structural studies, discussing the roles of long non-coding RNAs in the cell and how structure–function relationships might be used to elucidate further understanding. We then describe structural studies of other classes of RNAs using chemical probing, nuclear magnetic resonance, small-angle X-ray scattering, X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Next, we review early structural studies of long non-coding RNAs to date and describe the way forward for the structural biology of long non-coding RNAs in terms of cryo-EM.

RNA 3D Structure Prediction Using Coarse-Grained Models

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

An exon-biased biophysical approach and NMR spectroscopy define the secondary structure of a conserved helical element within the HOTAIR long non-coding RNA

HOTAIR is a large, multi-exon spliced non-coding RNA proposed to function as a molecular scaffold and competes with chromatin to bind to histone modification enzymes. Previous sequence analysis and biochemical experiments identified potential conserved regions and characterized the full length HOTAIR secondary structure. Here, we examine the thermodynamic folding properties and structural propensity of the individual exonic regions of HOTAIR using an array of biophysical methods and NMR spectroscopy. We demonstrate that different exons of HOTAIR contain variable degrees of heterogeneity, and identify one exonic region, exon 4, that adopts a stable and compact fold under low magnesium concentrations. Close agreement of NMR spectroscopy and chemical probing unambiguously confirm conserved base pair interactions within the structural element, termed helix 10 of exon 4, located within domain I of human HOTAIR. This combined exon-biased and integrated biophysical approach introduces a new strategy to examine conformational heterogeneity in lncRNAs and emphasizes NMR as a key method to validate base pair interactions and corroborate large RNA secondary structures.

Modeling and Predicting RNA Three-Dimensional Structures

Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis-Westhof extended base pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. We show how to take advantage of this knowledge to quickly predict three-dimensional structures of large RNA molecules and present the RNA-MoIP web server (http://rnamoip.cs.mcgill.ca) that streamlines the computational and visualization processes. Finally, we show recent advances in the prediction of local 3D motifs from sequence data with the BayesPairing software and discuss its impact toward complete 3D structure prediction.

Mechanisms and consequences of subcellular RNA localization across diverse cell types

Essentially all cells contain a variety of spatially restricted regions that are important for carrying out specialized functions. Often, these regions contain specialized transcriptomes that facilitate these functions by providing transcripts for localized translation. These transcripts play a functional role in maintaining cell physiology by enabling a quick response to changes in the cellular environment. Here, we review how RNA molecules are trafficked within cells, with a focus on the subcellular locations to which they are trafficked, mechanisms that regulate their transport and clinical disorders associated with misregulation of the process.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes

RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/. We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

RNA modifications in structure prediction - Status quo and future challenges

Chemical modifications of RNA nucleotides change their identity and characteristics and thus alter genetic and structural information encoded in the genomic DNA. tRNA and rRNA are probably the most heavily modified genes, and often depend on derivatization or isomerization of their nucleobases in order to correctly fold into their functional structures. Recent RNomics studies, however, report transcriptome wide RNA modification and suggest a more general regulation of structuredness of RNAs by this so called epitranscriptome. Modification seems to require specific substrate structures, which in turn are stabilized or destabilized and thus promote or inhibit refolding events of regulatory RNA structures. In this review, we revisit RNA modifications and the related structures from a computational point of view. We discuss known substrate structures, their properties such as sub-motifs as well as consequences of modifications on base pairing patterns and possible refolding events. Given that efficient RNA structure prediction methods for canonical base pairs have been established several decades ago, we review to what extend these methods allow the inclusion of modified nucleotides to model and study epitranscriptomic effects on RNA structures.

Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions

RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.