Efficient siRNA selection using hybridization thermodynamics

Spray-induced gene silencing to control plant pathogenic fungi: A step-by-step guide

RNA interference (RNAi)-based control technologies are gaining popularity as potential alternatives to synthetic fungicides in the ongoing effort to manage plant pathogenic fungi. Among these methods, spray-induced gene silencing (SIGS) emerges as particularly promising due to its convenience and feasibility for development. This approach is a new technology for plant disease management, in which double-stranded RNAs (dsRNAs) targeting essential or virulence genes are applied to plants or plant products and subsequently absorbed by plant pathogens, triggering a gene silencing effect and the inhibition of the infection process. Spray-induced gene silencing has demonstrated efficacy in laboratory settings against various fungal pathogens. However, as research progressed from the laboratory to the greenhouse and field environments, novel challenges arose, such as ensuring the stability of dsRNAs and their effective delivery to fungal targets. Here, we provide a practical guide to SIGS for the control of plant pathogenic fungi. This guide outlines the essential steps and considerations needed for designing and assessing dsRNA molecules. It also addresses key challenges inherent to SIGS, including delivery and stability of dsRNA molecules, and how nanoencapsulation of dsRNAs can aid in overcoming these obstacles. Additionally, the guide underscores existing knowledge gaps that warrant further research and aims to provide assistance to researchers, especially those new to the field, encouraging the advancement of SIGS for the control of a broad range of fungal pathogens.

Design of therapeutic siRNAs for potential application to infection with chikungunya virus

Emergence of the Chikungunya virus (CHIKV) is a new threat in the world. The disastrous effect of this virus and the unavailability of specific drugs complicated the control and management of the disease. The development of a siRNA-based drug using multiple computational tools could be a way out as one of its therapeutics. Currently, very few siRNAs against CHIKV have been computationally designed and published. Here, we considered various parts of the CHIKV genome encoding different essential protein-coding genes for designing siRNAs with a view to silencing them, thereby rendering the virus inactive. Seven potential primary siRNAs were constructed, of which, five are hereafter recommended to be used as a therapeutic tool against the virus.

An oligo walk to identify siRNAs against the circular Tau 12->7 RNA

Circular RNAs are associated with numerous diseases and recent evidence shows that they can be translated into proteins after undergoing RNA modification. Circular RNAs differ from their 'linear' mRNA counterparts in their backsplice site, allowing selective targeting using RNA interference, which however limits the options to place the siRNA. We tested all possible siRNAs against the backsplice site of the circTau 12->7 RNA after it was subjected to adenosine to inosine RNA editing, a modification that promotes translation of the circRNA. Most siRNAs reduced the circRNA and protein abundance, which however did not correlate. We identified an siRNA with an IC50 of 750 pmol efficacy on protein expression. This circRNA fulfilled six of the eight criteria for siRNAs targeting mRNAs. Thus, modified circRNAs expressing protein can be targeted with siRNAs, but their optimal sequence needs to be determined empirically.

Haemaphysalis longicornis subolesin controls the infection and transmission of severe fever with thrombocytopenia syndrome virus

Severe fever with thrombocytopenia syndrome (SFTS) caused by the SFTS virus (SFTSV) is an emerging tick-borne disease with a high mortality rate. Haemaphysalis longicornis is the primary reservoir and vector of SFTSV. Here, we found that targeting subolesin (SUB), an anti-tick vaccine candidate, affects the infection and transmission of SFTSV in H. longicornis. RNAi-mediated knockdown of SUB repressed SFTSV infection in the salivary glands but not in the gut of H. longicornis, which may be associated with the modulation of protein processing in endoplasmic reticulum revealed by transcriptomic analysis. Knockdown of SUB decreased the survival and engorgement rates of ticks and impaired the horizontal and co-feeding transmission of SFTSV. Furthermore, active immunization with recombinant SUB inhibited the co-feeding transmission of SFTSV, although it had no significant effect on the blood-feeding behavior of infected ticks. Collectively, these results provide a potential target for controlling SFTS and other tick-borne viral diseases.

siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user-friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA-related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer-aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non-commercial use.

Using the RNAstructure Software Package to Predict Conserved RNA Structures

The structures of many non-coding RNAs (ncRNA) are conserved by evolution to a greater extent than their sequences. By predicting the conserved structure of two or more homologous sequences, the accuracy of secondary structure prediction can be improved as compared to structure prediction for a single sequence. Here, we provide protocols for the use of four programs in the RNAstructure suite to predict conserved structures: Multilign, TurboFold, Dynalign, and PARTS. TurboFold iteratively aligns multiple homologous sequences and estimates the pairing probabilities for the conserved structure. Dynalign, PARTS, and Multilign are dynamic programming algorithms that simultaneously align sequences and identify the common secondary structure. Dynalign uses a pair of homologs and finds the lowest free energy common structure. PARTS uses a pair of homologs and estimates pairing probabilities from the base pairing probabilities estimated for each sequence. Multilign uses two or more homologs and finds the lowest free energy common structure using multiple pairwise calculations with Dynalign. It scales linearly with the number of sequences. We outline the strengths of each program. These programs can be run through web servers, on the command line, or with graphical user interfaces. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Predicting a structure conserved in three or more sequences with the RNAstructure web server Basic Protocol 2: Predicting a structure conserved in two sequences with the RNAstructure web server Alternative Protocol 1: Predicting a structure conserved in multiple sequences in the RNAstructure graphical user interface Alternative Protocol 2: Predicting a structure conserved in two sequences with Dynalign in the RNAstructure graphical user interface Alternative Protocol 3: Running TurboFold on the command line.

OligoFormer: an accurate and robust prediction method for siRNA design

MOTIVATION

RNA interference (RNAi) has become a widely used experimental approach for post-transcriptional regulation and is increasingly showing its potential as future targeted drugs. However, the prediction of highly efficient siRNAs (small interfering RNAs) is still hindered by dataset biases, the inadequacy of prediction methods, and the presence of off-target effects. To overcome these limitations, we propose an accurate and robust prediction method, OligoFormer, for siRNA design.

RESULTS

OligoFormer comprises three different modules including thermodynamic calculation, RNA-FM module, and Oligo encoder. Oligo encoder is the core module based on the transformer encoder. Taking siRNA and mRNA sequences as input, OligoFormer can obtain thermodynamic parameters, RNA-FM embedding, and Oligo embedding through these three modules, respectively. We carefully benchmarked OligoFormer against six comparable methods on siRNA efficacy datasets. OligoFormer outperforms all the other methods, with an average improvement of 9% in AUC, 6.6% in PRC, 9.8% in F1 score, and 5.1% in PCC compared to the best method among them in our inter-dataset validation. We also provide a comprehensive pipeline with prediction of siRNA efficacy and off-target effects using PITA score and TargetScan score. The ablation study shows RNA-FM module and thermodynamic parameters improved the performance and accelerated convergence of OligoFormer. The saliency maps by gradient backpropagation and base preference maps show certain base preferences in initial and terminal region of siRNAs.

AVAILABILITY AND IMPLEMENTATION

The source code of OligoFormer is freely available on GitHub at: https://github.com/lulab/OligoFormer. Docker image of OligoFormer is freely available on the docker hub at https://hub.docker.com/r/yilanbai/oligoformer.

From sequences to therapeutics: Using machine learning to predict chemically modified siRNA activity

Small interfering RNAs (siRNAs) exemplify the promise of genetic medicine in the discovery of novel therapeutic modalities. Their ability to selectively suppress gene expression makes them ideal candidates for the development of oligonucleotide pharmaceuticals. Recent advancements in machine learning (ML) have facilitated the design of unmodified siRNA and efficacy prediction. However, a model trained to predict the silencing activity of siRNAs with diverse chemical modification patterns is yet to be published despite the importance of such modifications in designing siRNAs with the potential to reach the level of clinical use. This study presents the first application of ML to efficiently classify chemically modified siRNAs on the basis of sequence and chemical modification patterns alone. Three algorithms were evaluated at three classification thresholds and compared according to sensitivity, specificity, consistency of feature weights with empirical knowledge, and performance using an external validation dataset. Finally, possible directions for future research were proposed.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites

BACKGROUND

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.

RESULTS

We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs.

CONCLUSIONS

CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

RNAi-Mediated Silencing in the Insect Cell-Baculovirus Expression System

RNA interference (RNAi) serves as an indispensable tool for gene function studies and has been substantiated through extensive research for its practical applications in the baculovirus expression vector system (BEVS). This chapter expands the RNAi toolkit in insect cell culture by including small interfering RNA (siRNA) in the protocol, in addition to the conventional use of double-stranded RNA (dsRNA). This chapter also brings attention to key design and reporting considerations, based on Minimum Information About an RNAi Experiment (MIARE) guidelines. Recommendations regarding online tools for dsRNA and siRNA design are provided, along with guidance on choosing suitable methods for measuring silencing outcomes.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy

Chemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. Identifying chemically modified siRNA sequences that effectively silence target genes remains challenging. Such determinations necessitate computational algorithms. Machine learning is a powerful predictive approach for tackling biological problems but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework applying machine learning to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous, two-threshold, partitioning approach, producing several combinations of classification threshold pairs. We then test the effects of different thresholds on random forest machine learning model performance using a novel evaluation metric accounting for class imbalances. We identify thresholds yielding a model with high predictive power, outperforming a linear model generated from the same data, that was predictive upon experimental evaluation. Using a novel model feature extraction method, we observe target site base importances and base preferences consistent with our current understanding of the siRNA-mediated silencing mechanism, with the random forest providing higher resolution than the linear model. This framework applies to any classification challenge involving small biological datasets, providing an opportunity to develop high-performing design algorithms for oligonucleotide therapies.

RNA Secondary Structure Analysis Using RNAstructure

RNAstructure is a user-friendly program for the prediction and analysis of RNA secondary structure. It is available as a web server, a program with a graphical user interface, or a set of command line tools. The programs are available for Microsoft Windows, macOS, or Linux. This article provides protocols for prediction of RNA secondary structure (using the web server, the graphical user interface, or the command line) and high-affinity oligonucleotide binding sites to a structured RNA target (using the graphical user interface). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Predicting RNA secondary structure using the RNAstructure web server Alternate Protocol 1: Predicting secondary structure and base pair probabilities using the RNAstructure graphical user interface Alternate Protocol 2: Predicting secondary structure and base pair probabilities using the RNAstructure command line interface Basic Protocol 2: Predicting binding affinities of oligonucleotides complementary to an RNA target using OligoWalk.

LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.

Therapeutic siRNA: State-of-the-Art and Future Perspectives

The highly specific induction of RNA interference-mediated gene knockdown, based on the direct application of small interfering RNAs (siRNAs), opens novel avenues towards innovative therapies. Two decades after the discovery of the RNA interference mechanism, the first siRNA drugs received approval for clinical use by the US Food and Drug Administration and the European Medicines Agency between 2018 and 2022. These are mainly based on an siRNA conjugation with a targeting moiety for liver hepatocytes, N-acetylgalactosamine, and cover the treatment of acute hepatic porphyria, transthyretin-mediated amyloidosis, hypercholesterolemia, and primary hyperoxaluria type 1. Still, the development of siRNA therapeutics faces several challenges and issues, including the definition of optimal siRNAs in terms of target, sequence, and chemical modifications, siRNA delivery to its intended site of action, and the absence of unspecific off-target effects. Further siRNA drugs are in clinical studies, based on different delivery systems and covering a wide range of different pathologies including metabolic diseases, hematology, infectious diseases, oncology, ocular diseases, and others. This article reviews the knowledge on siRNA design and chemical modification, as well as issues related to siRNA delivery that may be addressed using different delivery systems. Details on the mode of action and clinical status of the various siRNA therapeutics are provided, before giving an outlook on issues regarding the future of siRNA drugs and on their potential as one emerging standard modality in pharmacotherapy. Notably, this may also cover otherwise un-druggable diseases, the definition of non-coding RNAs as targets, and novel concepts of personalized and combination treatment regimens.

CaSilico: A versatile CRISPR package for in silico CRISPR RNA designing for Cas12, Cas13, and Cas14

The efficiency of the CRISPR-Cas system is highly dependent on well-designed CRISPR RNA (crRNA). To facilitate the use of various types of CRISPR-Cas systems, there is a need for the development of computational tools to design crRNAs which cover different CRISPR-Cas systems with off-target analysis capability. Numerous crRNA design tools have been developed, but nearly all of them are dedicated to design crRNA for genome editing. Hence, we developed a tool matching the needs of both beginners and experts, named CaSilico, which was inspired by the limitations of the current crRNA design tools for designing crRNAs for Cas12, Cas13, and Cas14 CRISPR-Cas systems. This tool considers a comprehensive list of the principal rules that are not yet well described to design crRNA for these types. Using a list of important features such as mismatch tolerance rules, self-complementarity, GC content, frequency of cleaving base around the target site, target accessibility, and PFS (protospacer flanking site) or PAM (protospacer adjacent motif) requirement, CaSilico searches all potential crRNAs in a user-input sequence. Considering these features help users to rank all crRNAs for a sequence and make an informed decision about whether a crRNA is suited for an experiment or not. Our tool is sufficiently flexible to tune some key parameters governing the design of crRNA and identification of off-targets, which can lead to an increase in the chances of successful CRISPR-Cas experiments. CaSilico outperforms previous crRNA design tools in the following aspects: 1) supporting any reference genome/gene/transcriptome for which an FASTA file is available; 2) designing crRNAs that simultaneously target multiple sequences through conserved region detection among a set of sequences; 3) considering new CRISPR-Cas subtypes; and 4) reporting a list of different features for each candidate crRNA, which can help the user to select the best one. Given these capabilities, CaSilico addresses end-user concerns arising from the use of sophisticated bioinformatics algorithms and has a wide range of potential research applications in different areas, especially in the design of crRNA for pathogen diagnosis. CaSilico was successfully applied to design crRNAs for different genes in the SARS-CoV-2 genome, as some of the crRNAs have been experimentally tested in the previous studies.

Nearest neighbor rules for RNA helix folding thermodynamics: improved end effects

Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated 5′GGUC3′ paired to 3′CUGG5′ separately from other 5′GU3′/3′UG5′ stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37°C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.

Design of siRNA molecules for silencing of membrane glycoprotein, nucleocapsid phosphoprotein, and surface glycoprotein genes of SARS-CoV2

The global COVID-19 pandemic caused by SARS-CoV2 infected millions of people and resulted in more than 4 million deaths worldwide. Apart from vaccines and drugs, RNA silencing is a novel approach for treating COVID-19. In the present study, siRNAs were designed for the conserved regions targeting three structural genes, M, N, and S, from forty whole-genome sequences of SARS-CoV2 using four different software, RNAxs, siDirect, i-Score Designer, and OligoWalk. Only siRNAs which were predicted in common by all the four servers were considered for further shortlisting. A multistep filtering approach has been adopted in the present study for the final selection of siRNAs by the usage of different online tools, viz., siRNA scales, MaxExpect, DuplexFold, and SMEpred. All these web-based tools consider several important parameters for designing functional siRNAs, e.g., target-site accessibility, duplex stability, position-specific nucleotide preference, inhibitory score, thermodynamic parameters, GC content, and efficacy in cleaving the target. In addition, a few parameters like GC content and dG value of the entire siRNA were also considered for shortlisting of the siRNAs. Antisense strands were subjected to check for any off-target similarities using BLAST. Molecular docking was carried out to study the interactions of guide strands with AGO2 protein. A total of six functional siRNAs (two for each gene) have been finally selected for targeting M, N, and S genes of SARS-CoV2. The siRNAs have not shown any off-target effects, interacted with the domain(s) of AGO2 protein, and were efficacious in cleaving the target mRNA. However, the siRNAs designed in the present study need to be tested in vitro and in vivo in the future.

A Comprehensive Computational Investigation into the Conserved Virulent Proteins of Shigella species Unveils Potential Small-Interfering RNA Candidates as a New Therapeutic Strategy against Shigellosis

Shigella species account for the second-leading cause of deaths due to diarrheal diseases among children of less than 5 years of age. The emergence of multi-drug-resistant Shigella isolates and the lack of availability of Shigella vaccines have led to the pertinence in the efforts made for the development of new therapeutic strategies against shigellosis. Consequently, designing small-interfering RNA (siRNA) candidates against such infectious agents represents a novel approach to propose new therapeutic candidates to curb the rampant rise of anti-microbial resistance in such pathogens. In this study, we analyzed 264 conserved sequences from 15 different conserved virulence genes of Shigella sp., through extensive rational validation using a plethora of first-generation and second-generation computational algorithms for siRNA designing. Fifty-eight siRNA candidates were obtained by using the first-generation algorithms, out of which only 38 siRNA candidates complied with the second-generation rules of siRNA designing. Further computational validation showed that 16 siRNA candidates were found to have a substantial functional efficiency, out of which 11 siRNA candidates were found to be non-immunogenic. Finally, three siRNA candidates exhibited a sterically feasible three-dimensional structure as exhibited by parameters of nucleic acid geometry such as: the probability of wrong sugar puckers, bad backbone confirmations, bad bonds, and bad angles being within the accepted threshold for stable tertiary structure. Although the findings of our study require further wet-lab validation and optimization for therapeutic use in the treatment of shigellosis, the computationally validated siRNA candidates are expected to suppress the expression of the virulence genes, namely: IpgD (siRNA 9) and OspB (siRNA 15 and siRNA 17) and thus act as a prospective tool in the RNA interference (RNAi) pathway. However, the findings of our study require further wet-lab validation and optimization for regular therapeutic use for treatment of shigellosis.

Secondary structure prediction for RNA sequences including N6-methyladenosine

There is increasing interest in the roles of covalently modified nucleotides in RNA. There has been, however, an inability to account for modifications in secondary structure prediction because of a lack of software and thermodynamic parameters. We report the solution for these issues for N⁶-methyladenosine (m⁶A), allowing secondary structure prediction for an alphabet of A, C, G, U, and m⁶A. The RNAstructure software now works with user-defined nucleotide alphabets of any size. We also report a set of nearest neighbor parameters for helices and loops containing m⁶A, using experiments. Interestingly, N⁶-methylation decreases folding stability for adenosines in the middle of a helix, has little effect on folding stability for adenosines at the ends of helices, and increases folding stability for unpaired adenosines stacked on a helix. We demonstrate predictions for an N⁶-methylation-activated protein recognition site from MALAT1 and human transcriptome-wide effects of N⁶-methylation on the probability of adenosine being buried in a helix.

RNA folding free energy nearest neighbor parameters were determined for sequences with the nucleotide m⁶A. The RNAstructure software package can accommodate modified nucleotides, enabling secondary structure prediction of sequences with m⁶A.

RNA Structure Prediction, Analysis, and Design: An Introduction to Web-Based Tools

Understanding RNA structure has become critical in the study of RNA in their roles as mediators of biological processes. To aid in these studies, computational algorithms that utilize thermodynamics have been developed to predict RNA secondary structure. Due to the importance of intermolecular interactions, the algorithms have been expanded to determine and predict RNA-RNA hybridization. This chapter discusses popular webservers with the tools for RNA secondary structure prediction, RNA-RNA hybridization, and design. We address key features that distinguish common-functioning programs and their purposes for the interests of the user. Ultimately, we hope this review elucidates web-based tools researchers may take advantage of in their investigations of RNA structure and function.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

An in-silico approach to design potential siRNAs against the ORF57 of Kaposi's sarcoma-associated herpesvirus

Kaposi's sarcoma-associated herpesvirus (KSHV) is one of the few human oncogenic viruses, which causes a variety of malignancies, including Kaposi's sarcoma, multicentric Castleman disease, and primary effusion lymphoma, particularly in human immunodeficiency virus patients. The currently available treatment options cannot always prevent the invasion and dissemination of this virus. In recent times, siRNA-based therapeutics are gaining prominence over conventional medications as siRNA can be designed to target almost any gene of interest. The ORF57 is a crucial regulatory protein for lytic gene expression of KSHV. Disruption of this gene translation will inevitably inhibit the replication of the virus in the host cell. Therefore, the ORF57 of KSHV could be a potential target for designing siRNA-based therapeutics. Considering both sequence preferences and target site accessibility, several online tools (i-SCORE Designer, Sfold web server) had been utilized to predict the siRNA guide strand against the ORF57. Subsequently, off-target filtration (BLAST), conservancy test (fuzznuc), and thermodynamics analysis (RNAcofold, RNAalifold, and RNA Structure web server) were also performed to select the most suitable siRNA sequences. Finally, two siRNAs were identified that passed all of the filtration phases and fulfilled the thermodynamic criteria. We hope that the siRNAs predicted in this study would be helpful for the development of new effective therapeutics against KSHV.

LazySampling and LinearSampling: Fast Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used. However, this algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) the sampling phase repeatedly recalculates many redundant recursions already done during the partition function phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent stochastic sampling from being used for very long RNAs such as the full genomes of SARS-CoV-2. To address these problems, we first adopt a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework, among which the LazySampling algorithm is the fastest by eliminating redundant work in the sampling phase via on-demand caching. Based on LazySampling, we further replace the cubic-time partition function by a linear-time approximate one, and derive LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard one. For instance, LinearSampling is 176Ã- faster (38.9s vs. 1.9h) than Vienna RNAsubopt on the full genome of Ebola virus (18,959 nt ). More importantly, LinearSampling is the first RNA structure sampling algorithm to scale up to the full-genome of SARS-CoV-2 without local window constraints, taking only 69.2 seconds on its reference sequence (29,903 nt ). The resulting sample correlates well with the experimentally-guided structures. On the SARS-CoV-2 genome, LinearSampling finds 23 regions of 15 nt with high accessibilities, which are potential targets for COVID-19 diagnostics and drug design. See code: https://github.com/LinearFold/LinearSampling.

LinearTurboFold: Linear-Time Global Prediction of Conserved Structures for RNA Homologs with Applications to SARS-CoV-2

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length, and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt ) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurbo-Fold's purely in silico prediction not only is close to experimentally-guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' UTRs (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies novel conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, siRNAs, CRISPR-Cas13 guide RNAs and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies, and will be a useful tool in fighting the current and future pandemics.

SIGNIFICANCE STATEMENT

Conserved RNA structures are critical for designing diagnostic and therapeutic tools for many diseases including COVID-19. However, existing algorithms are much too slow to model the global structures of full-length RNA viral genomes. We present LinearTurboFold, a linear-time algorithm that is orders of magnitude faster, making it the first method to simultaneously fold and align whole genomes of SARS-CoV-2 variants, the longest known RNA virus (∼30 kilobases). Our work enables unprecedented global structural analysis and captures long-range interactions that are out of reach for existing algorithms but crucial for RNA functions. LinearTurboFold is a general technique for full-length genome studies and can help fight the current and future pandemics.

Ribosome as a Translocase and Helicase

During protein synthesis, ribosome moves along mRNA to decode one codon after the other. Ribosome translocation is induced by a universally conserved protein, elongation factor G (EF-G) in bacteria and elongation factor 2 (EF-2) in eukaryotes. EF-G-induced translocation results in unwinding of the intramolecular secondary structures of mRNA by three base pairs at a time that renders the translating ribosome a processive helicase. Professor Alexander Sergeevich Spirin has made numerous seminal contributions to understanding the molecular mechanism of translocation. Here, we review Spirin's insights into the ribosomal translocation and recent advances in the field that stemmed from Spirin's pioneering work. We also discuss key remaining challenges in studies of translocase and helicase activities of the ribosome.

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.

In Vitro Inhibitory Analysis of Rationally Designed siRNAs against MERS-CoV Replication in Huh7 Cells

MERS-CoV was identified for the first time in Jeddah, Saudi Arabia in 2012 in a hospitalized patient. This virus subsequently spread to 27 countries with a total of 939 deaths and 2586 confirmed cases and now has become a serious concern globally. Camels are well known for the transmission of the virus to the human population. In this report, we have discussed the prediction, designing, and evaluation of potential siRNA targeting the ORF1ab gene for the inhibition of MERS-CoV replication. The online software, siDirect 2.0 was used to predict and design the siRNAs, their secondary structure and their target accessibility. ORF1ab gene folding was performed by RNAxs and RNAfold software. A total of twenty-one siRNAs were selected from 462 siRNAs according to their scoring and specificity. siRNAs were evaluated in vitro for their cytotoxicity and antiviral efficacy in Huh7 cell line. No significant cytotoxicity was observed for all siRNAs in Huh7 cells. The in vitro study showed the inhibition of viral replication by three siRNAs. The data generated in this study provide preliminary and encouraging information to evaluate the siRNAs separately as well as in combination against MERS-CoV replication in other cell lines. The prediction of siRNAs using online software resulted in the filtration and selection of potential siRNAs with high accuracy and strength. This computational approach resulted in three effective siRNAs that can be taken further to in vivo animal studies and can be used to develop safe and effective antiviral therapies for other prevalent disease-causing viruses.

RNA secondary structure prediction using deep learning with thermodynamic integration

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.

Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.

siRNA Design to Silence the 3'UTR Region of Zika Virus

The disease caused by the Zika virus (ZIKV) has positioned itself as one of the main public health problems in Mexico. One of the main reasons is it causes microcephaly and other birth defects. The transmission of ZIKV is through Aedes aegypti and Ae. albopictus mosquitoes, which are found in a larger space of the national territory. In addition, it can also be transmitted via blood transfusion, sexual relations, and maternal-fetal route. So far, there are no vaccines or specific treatments to deal with this infection. Currently, some new therapeutics such as small interfering RNAs (siRNAs) are able to regulate or suppress transcription in viruses. Therefore, in this project, an in silico siRNA was designed for the 3'UTR region of ZIKV via bioinformatics tools. The designed siRNA was synthesized and transfected into the C6/36 cell line, previously infected with ZIKV in order to assess the ability of the siRNA to inhibit viral replication. The designed siRNA was able to inhibit significantly (p < 0.05) ZIKV replication; this siRNA could be considered a potential therapeutic towards the disease that causes ZIKV and the medical problems generated.

LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

MOTIVATION

RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime.

RESULTS

Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart).

AVAILABILITY AND IMPLEMENTATION

Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Making ends meet: New functions of mRNA secondary structure

The 5' cap and 3' poly(A) tail of mRNA are known to synergistically regulate mRNA translation and stability. Recent computational and experimental studies revealed that both protein-coding and non-coding RNAs will fold with extensive intramolecular secondary structure, which will result in close distances between the sequence ends. This proximity of the ends is a sequence-independent, universal property of most RNAs. Only low-complexity sequences without guanosines are without secondary structure and exhibit end-to-end distances expected for RNA random coils. The innate proximity of RNA ends might have important biological implications that remain unexplored. In particular, the inherent compactness of mRNA might regulate translation initiation by facilitating the formation of protein complexes that bridge mRNA 5' and 3' ends. Additionally, the proximity of mRNA ends might mediate coupling of 3' deadenylation to 5' end mRNA decay. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems Translation > Translation Regulation.

Generating DNA Expression Cassettes Encoding Multimeric Artificial MicroRNA Precursors

RNA interference (RNAi) is a promising tool for the treatment of chronic viral infection, such as that caused by the hepatitis B virus (HBV). RNAi activators, including expressed primary microRNA (pri-miRNA) mimics, can effectively silence viral gene expression and thereby inhibit viral replication. Here we describe a protocol for the design, generation and functional assessment of cassettes encoding effective single and multimeric pri-miRNA mimics. Artificial miRNAs targeting viral genes can be identified in silico and used to design corresponding pri-miRNA mimics. A two-step generation and TA cloning protocol can be used to produce single mimics, while the strategic use of restriction sites enables concatenation of mimics in a sub-cloning protocol. Basic gene silencing function of pri-miRNA mimics in cell culture can then be assessed using a dual luciferase assay and appropriate minimal targets. The methods described here for the generation of effective pri-miRNA mimics targeting HBV can be applied in the silencing of other viral or endogenous genes.

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

MOTIVATION

Predicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications.

RESULTS

We present a novel alternative O(n3)-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in O(n) time and O(n) space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100 000nt).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico prediction of short hairpin RNA and in vitro silencing of activin receptor type IIB in chicken embryo fibroblasts by RNA interference

Gene silencing by RNA interference is extensively used reverse genetic approach to analyse the implications of any gene in mammalian systems. The silencing of the Activin type IIB receptor belonging to transforming growth factor beta superfamily has demonstrated increase in muscle growth in many species. We designed five short hairpin RNA constructs targeting coding region of chicken ACTRIIB. All the shRNAs were transfected into chicken embryo fibroblast cells and evaluated their silencing efficiency by real time PCR and western blotting. Initially the computational analysis of target region and shRNA constructs was undertaken to predict sequence based features (secondary structures, GC% and H-b index) and thermodynamic features (ΔG_overall, ΔG_duplex, ΔG_break-target, ΔG_{intra-oligomer}, ΔG_{inter-oligomer} and ΔΔG_ends). We determined that all these predicted features were associated with shRNA efficacy. The invitro analysis of shRNA constructs exhibited significant (P < 0.05) reduction in the levels of ACTRIIB at mRNA and protein level. The knock down efficiency of shRNAs varied significantly (P < 0.001) from 83% (shRNA 1) to 43% (shRNA 5). All the shRNAs up regulated the myogenic pathway associated genes (MyoD and MyoG) significantly (P < 0.05). There was significant (P < 0.05) up-regulation of IFNA, IFNB and MHCII transcripts. The ACTRIIB expression was inversely associated with the expression of myogenic pathway and immune response genes. The anti ACTRIIB shRNA construct 1 and 3 exhibited maximum knock down efficiency with minimal interferon response, and can be used for generating ACTRIIB knockdown chicken with higher muscle mass.

PinMol: Python application for designing molecular beacons for live cell imaging of endogenous mRNAs

Molecular beacons are nucleic acid oligomers labeled with a fluorophore and a quencher that fold in a hairpin-shaped structure, which fluoresce only when bound to their target RNA. They are used for the visualization of endogenous mRNAs in live cells. Here, we report a Python program (PinMol) that designs molecular beacons best suited for live cell imaging by using structural information from secondary structures of the target RNA, predicted via energy minimization approaches. PinMol takes into account the accessibility of the targeted regions, as well as the inter- and intramolecular interactions of each selected probe. To demonstrate its applicability, we synthesized an oskar mRNA-specific molecular beacon (osk1236), which is selected by PinMol to target a more accessible region than a manually designed oskar-specific molecular beacon (osk2216). We previously demonstrated osk2216 to be efficient in detecting oskar mRNA in in vivo experiments. Here, we show that osk1236 outperformed osk2216 in live cell imaging experiments.

mRNAs and lncRNAs intrinsically form secondary structures with short end-to-end distances

The 5' and 3' termini of RNA play important roles in many cellular processes. Using Förster resonance energy transfer (FRET), we show that mRNAs and lncRNAs have an intrinsic propensity to fold in the absence of proteins into structures in which the 5' end and 3' end are ≤7 nm apart irrespective of mRNA length. Computational estimates suggest that the inherent proximity of the ends is a universal property of most mRNA and lncRNA sequences. Only guanosine-depleted RNA sequences with low sequence complexity are unstructured and exhibit end-to-end distances expected for the random coil conformation of RNA. While the biological implications remain to be explored, short end-to-end distances could facilitate the binding of protein factors that regulate translation initiation by bridging mRNA 5' and 3' ends. Furthermore, our studies provide the basis for measuring, computing and manipulating end-to-end distances and secondary structure in RNA in research and biotechnology.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.

Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions

Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA–RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA–RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA–mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs.

Effect of RNA interference of the expression of HMGA2 on the proliferation and invasion ability of ACHN renal cell carcinoma cells

This aim of the present study was to observe the effect of high mobility group AT-hook 2 (HMGA2) on the proliferation and invasion ability of ACHN renal cell carcinoma (RCC) cells. Human ACHN cells, an RCC cell line, and HKC normal human renal tubular epithelial cells were cultured. HMGA2 small interfering (si)RNA, Mock-siRNA and their negative control group were designed and synthesized. Subsequently, the ACHN cells were transiently transfected using RNA interference technology. Finally, the mRNA and protein expression levels of HMGA2 were detected using reverse transcription-polymerase chain reaction and western blot analyses. The proliferation ability of the ACHN cells was determined using MTT, and ACHN cell invasion ability was detected using the Transwell method. The results showed that the mRNA and protein expression levels of HMGA2 in the ACHN cells were considerably higher, compared with those in the HKC cells (P<0.01). The RCC cells, in which the expression of HMGA2 was specifically silenced, was successfully constructed. The proliferation rate of cells in the HMGA2-siRNA group was significantly lower, compared with that of cells in the Mock-siRNA group and control group at 24, 48, 72 and 96 h post-transfection (P<0.05). The invasion ability of cells in the HMGA2-siRNA group was significantly lower, compared with that of cells in the Mock-siRNA group and control group (P<0.05) 48 h following transfection. Therefore, the HMGA2 gene may function as an oncogene in the occurrence and development of RCC, and provide specific targets for the targeted therapy of RCC. Further detailed investigations of the HMGA2 gene are important for future gene therapy of RCC.

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.

Terminal Duplex Stability and Nucleotide Identity Differentially Control siRNA Loading and Activity in RNA Interference

Efficient short interfering RNA (siRNA)-mediated gene silencing requires selection of a sequence that is complementary to the intended target and possesses sequence and structural features that encourage favorable functional interactions with the RNA interference (RNAi) pathway proteins. In this study, we investigated how terminal sequence and structural characteristics of siRNAs contribute to siRNA strand loading and silencing activity and how these characteristics ultimately result in a functionally asymmetric duplex in cultured HeLa cells. Our results reiterate that the most important characteristic in determining siRNA activity is the 5' terminal nucleotide identity. Our findings further suggest that siRNA loading is controlled principally by the hybridization stability of the 5' terminus (Nucleotides: 1-2) of each siRNA strand, independent of the opposing terminus. Postloading, RNA-induced silencing complex (RISC)-specific activity was found to be improved by lower hybridization stability in the 5' terminus (Nucleotides: 3-4) of the loaded siRNA strand and greater hybridization stability toward the 3' terminus (Nucleotides: 17-18). Concomitantly, specific recognition of the 5' terminal nucleotide sequence by human Argonaute 2 (Ago2) improves RISC half-life. These findings indicate that careful selection of siRNA sequences can maximize both the loading and the specific activity of the intended guide strand.

Predicting RNA-RNA Interactions Using RNAstructure

RNA-RNA binding is a required step for many regulatory and catalytic processes in the cell. Identifying RNA-RNA hybridization sites is challenging because of the competition between intramolecular and intermolecular structure formation. A complete picture of RNA-RNA binding includes an understanding of single-stranded folding and binding site accessibility, and is strongly concentration-dependent. This chapter provides guidance for using RNAstructure to predict RNA-RNA binding sites and RNA-RNA structures, utilizing free energy minimization and partition function calculations. RNAstructure is freely available at http://rna.urmc.rochester.edu/RNAstructure.html .

AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure

MOTIVATION

There are numerous examples of RNA-RNA complexes, including microRNA-mRNA and small RNA-mRNA duplexes for regulation of translation, guide RNA interactions with target RNA for post-transcriptional modification and small nuclear RNA duplexes for splicing. Predicting the base pairs formed between two interacting sequences remains difficult, at least in part because of the competition between unimolecular and bimolecular structure.

RESULTS

Two algorithms were developed for improved prediction of bimolecular RNA structure that consider the competition between self-structure and bimolecular structure. These algorithms utilize two novel approaches to evaluate accessibility: free energy density minimization and pseudo-energy minimization. Free energy density minimization minimizes the folding free energy change per nucleotide involved in an intermolecular secondary structure. Pseudo-energy minimization (called AccessFold) minimizes the sum of free energy change and a pseudo-free energy penalty for bimolecular pairing of nucleotides that are unlikely to be accessible for bimolecular structure. The pseudo-free energy, derived from unimolecular pairing probabilities, is applied per nucleotide in bimolecular pairs, and this approach is able to predict binding sites that are split by unimolecular structures. A benchmark set of 17 bimolecular RNA structures was assembled to assess structure prediction. Pseudo-energy minimization provides a statistically significant improvement in sensitivity over the method that was found in a benchmark to be the most accurate previously available method, with an improvement from 36.8% to 57.8% in mean sensitivity for base pair prediction.

AVAILABILITY AND IMPLEMENTATION

Pseudo-energy minimization is available for download as AccessFold, under an open-source license and as part of the RNAstructure package, at: http://rna.urmc.rochester.edu/RNAstructure.html

CONTACT

david_mathews@urmc.rochester.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving RNA secondary structure prediction with structure mapping data

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide.

High-throughput characterization of protein-RNA interactions

RNA-binding proteins (RBPs) are important regulators of eukaryotic gene expression. Genomes typically encode dozens to hundreds of proteins containing RNA-binding domains, which collectively recognize diverse RNA sequences and structures. Recent advances in high-throughput methods for assaying the targets of RBPs in vitro and in vivo allow large-scale derivation of RNA-binding motifs as well as determination of RNA–protein interactions in living cells. In parallel, many computational methods have been developed to analyze and interpret these data. The interplay between RNA secondary structure and RBP binding has also been a growing theme. Integrating RNA–protein interaction data with observations of post-transcriptional regulation will enhance our understanding of the roles of these important proteins.

Differential RNAi responses of Nicotiana benthamiana individuals transformed with a hairpin-inducing construct during Plum pox virus challenge

Gene silencing and large-scale small RNA analysis can be used to develop RNA interference (RNAi)-based resistance strategies for Plum pox virus (PPV), a high impact disease of Prunus spp. In this study, a pPPViRNA hairpin-inducing vector harboring two silencing motif-rich regions of the PPV coat protein (CP) gene was evaluated in transgenic Nicotiana benthamiana (NB) plants. Wild-type NB plants infected with a chimeric PPV virus (PPV::GFP) exhibited affected leaves with mosaic chlorosis congruent to GFP fluorescence at 21 day post-inoculation; transgenic lines depicted a range of phenotypes from fully resistant to susceptible. ELISA values and GFP fluorescence intensities were used to select transgenic-resistant (TG-R) and transgenic-susceptible (TG-S) lines for further characterization of small interfering RNAs (siRNAs) by large-scale small RNA sequencing. In infected TG-S and untransformed (WT) plants, the observed siRNAs were nearly exclusively 21- and 22-nt siRNAs that targeted the whole PPV::GFP genome; 24-nt siRNAs were absent in these individuals. Challenged TG-R plants accumulated a full set of 21- to 24-nt siRNAs that were primarily associated with the selected motif-rich regions, indicating that a trans-acting siRNAs process prevented viral multiplication. BLAST analysis identified 13 common siRNA clusters targeting the CP gene. 21-nt siRNA sequences were associated with the 22-nt siRNAs and the scarce 23- and 24-nt molecules in TG-S plants and with most of the observed 22-, 23-, and 24-nt siRNAs in TG-R individuals. These results validate the use of a multi-hot spot silencing vector against PPV and elucidate the molecules by which hairpin-inducing vectors initiate RNAi in vivo.