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MOTIVATION: Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. METHODS: We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. RESULTS: Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. AVAILABILITY: Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.

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Pseudoknots assume various functions including stimulation of -1 programmed ribosomal frameshifting (PRF) or stop codon readthrough (SCR) in RNA viruses. These pseudoknots vary greatly in sizes and structural complexities. Recent biochemical and structural studies confirm the three-stemmed pseudoknots as the -1 PRF stimulators in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses. We reexamined previously reported -1 PRF or SCR stimulating pseudoknots, especially those containing a relatively long connecting loop between the two pseudoknot-forming stems, for their ability to form elaborated structures. Many potential elaborated pseudoknots were identified that contain one or more of the following extra structural elements: stem-loop, embedded pseudoknot, kissing hairpins, and additional loop-loop interactions. The elaborated pseudoknots are found in several different virus families that utilize either the -1 PRF or SCR recoding mechanisms. Model-building studies were performed to not only establish the structural feasibility of the elaborated pseudoknots but also reveal potential additional structural features that cannot be readily inferred from the predicted secondary structures. Some of the structures, such as embedded double pseudoknots and compact loop-loop pseudoknots mediated by the previously established common pseudoknot motif-1 (CPK-1), represent the first of its kind in the literatures. By advancing discovery of new functional RNA structures, we significantly expand the repertoire of known elaborated pseudoknots that could potentially play a role in -1 PRF and SCR regulation. These results contribute to a better understanding of RNA structures in general, facilitating the design of engineering RNA molecules with certain desired functions.Communicated by Ramaswamy H. Sarma.

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Although RNA secondary structure prediction is a textbook application of dynamic programming (DP) and routine task in RNA structure analysis, it remains challenging whenever pseudoknots come into play. Since the prediction of pseudoknotted structures by minimizing (realistically modelled) energy is NP-hard, specialized algorithms have been proposed for restricted conformation classes that capture the most frequently observed configurations. To achieve good performance, these methods rely on specific and carefully hand-crafted DP schemes. In contrast, we generalize and fully automatize the design of DP pseudoknot prediction algorithms. For this purpose, we formalize the problem of designing DP algorithms for an (infinite) class of conformations, modeled by (a finite number of) fatgraphs, and automatically build DP schemes minimizing their algorithmic complexity. We propose an algorithm for the problem, based on the tree-decomposition of a well-chosen representative structure, which we simplify and reinterpret as a DP scheme. The algorithm is fixed-parameter tractable for the treewidth tw of the fatgraph, and its output represents a [Formula: see text] algorithm (and even possibly [Formula: see text] in simple energy models) for predicting the MFE folding of an RNA of length n. We demonstrate, for the most common pseudoknot classes, that our automatically generated algorithms achieve the same complexities as reported in the literature for hand-crafted schemes. Our framework supports general energy models, partition function computations, recursive substructures and partial folding, and could pave the way for algebraic dynamic programming beyond the context-free case.

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Nowadays there are numerous discovered natural RNA variations participating in different cellular processes and artificial RNA, e. g., aptamers, riboswitches. One of the required tasks in the investigation of their functions and mechanism of influence on cells and interaction with targets is the prediction of RNA secondary structures. The classic thermodynamic-based prediction algorithms do not consider the specificity of biological folding and deep learning methods that were designed to resolve this issue suffer from homology-based methods problems. Herein, we present a method for RNA secondary structure prediction based on deep learning - AliNA (ALIgned Nucleic Acids). Our method successfully predicts secondary structures for non-homologous to train-data RNA families thanks to usage of the data augmentation techniques. Augmentation extends existing datasets with easily-accessible simulated data. The proposed method shows a high quality of prediction across different benchmarks including pseudoknots. The method is available on GitHub for free (https://github.com/Arty40m/AliNA).

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Long non-coding RNAs (lncRNAs) are recently-discovered transcripts involved in gene expression regulation and associated with diseases. Despite the unprecedented molecular complexity of these transcripts, recent studies of the secondary and tertiary structure of lncRNAs are starting to reveal the principles of lncRNA structural organization, with important functional implications. It therefore starts to be possible to analyze lncRNA structures systematically. Here, using a set of prototypical and medically-relevant lncRNAs of known secondary structure, we specifically catalogue the distribution and structural environment of one of the first-identified and most frequently occurring non-canonical Watson-Crick interactions, the G·U base pair. We compare the properties of G·U base pairs in our set of lncRNAs to those of the G·U base pairs in other well-characterized transcripts, like rRNAs, tRNAs, ribozymes, and riboswitches. Furthermore, we discuss how G·U base pairs in these targets participate in establishing interactions with proteins or miRNAs, and how they enable lncRNA tertiary folding by forming intramolecular or metal-ion interactions. Finally, by identifying highly-G·U-enriched regions of yet unknown function in our target lncRNAs, we provide a new rationale for future experimental investigation of these motifs, which will help obtain a more comprehensive understanding of lncRNA functions and molecular mechanisms in the future.

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Clostridium difficile frequently causes an infectious disease known as Clostridium difficile infection (CDI), and there is an urgent need for the development of more effective rapid diagnostic tests for CDI. Previously we have developed an RNA-cleaving fluorogenic DNAzyme (RFD) probe, named RFD-CD1, that is capable of detecting a specific strain of C. difficile but is too specific to recognize other pathogenic C. difficile strains. To overcome this issue, herein we report RFD-CD2, another RFD that is not only highly specific to C. difficile but also capable of recognizing diverse pathogenic C. difficile strains. Extensive sequence and structure characterization establishes a pseudoknot structure and a significantly minimized sequence for RFD-CD2. As a fluorescent sensor, RFD-CD2 can detect C. difficile at a concentration as low as 100 CFU/mL, thus making this DNAzyme an attractive molecular probe for rapid diagnosis of CDI caused by diverse strains of C. difficile.

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Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that is, the architecture, to define their parameter space. However, architecture dependency has not been sufficiently investigated, especially for pseudoknotted secondary structures. In this study, we propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that do not depend on the architecture of RNA secondary structures, and then implement this approach using two maximum expected accuracy (MEA)-based decoding algorithms: Nussinov-style decoding for pseudoknot-free structures and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm outperforms existing methods in prediction accuracy.

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BACKGROUND: Due to its key role in various biological processes, RNA secondary structures have always been the focus of in-depth analyses, with great efforts from mathematicians and biologists, to find a suitable abstract representation for modelling its functional and structural properties. One contribution is due to Kauffman and Magarshak, who modelled RNA secondary structures as mathematical objects constructed in link theory: tangles of the Brauer Monoid. In this paper, we extend the tangle-based model with its minimal prime factorization, useful to analyze patterns that characterize the RNA secondary structure. RESULTS: By leveraging the mapping between RNA and tangles, we prove that the prime factorizations of tangle-based models share some patterns with RNA folding's features. We analyze the E. coli tRNA and provide some visual examples of interesting patterns. CONCLUSIONS: We formulate an open question on the nature of the class of equivalent factorizations and discuss some research directions in this regard. We also propose some practical applications of the tangle-based method to RNA classification and folding prediction as a useful tool for learning algorithms, even though the full factorization is not known.

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RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.

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BACKGROUND: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance. RESULTS: The algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively. CONCLUSIONS: The flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.

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Ribozymes are RNAs that catalyze reactions. They occur in nature, and can also be evolved in vitro to catalyze novel reactions. This chapter provides detailed protocols for using inverse folding software to design a ribozyme sequence that will fold to a known ribozyme secondary structure and for testing the catalytic activity of the sequence experimentally. This protocol is able to design sequences that include pseudoknots, which is important as all naturally occurring full-length ribozymes have pseudoknots. The starting point is the known pseudoknot-containing secondary structure of the ribozyme and knowledge of any nucleotides whose identity is required for function. The output of the protocol is a set of sequences that have been tested for function. Using this protocol, we were previously successful at designing highly active double-pseudoknotted HDV ribozymes.

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New biochemical screening and design based technology are used to identify the small molecules in targeting RNA. These approaches has develop potential drug like small molecule for RNA-targeted therapeutics. Chemical Reactivity Theory (CRT) is used to study these drug-like, biologically active small molecules that target RNA. Twenty two small molecules based on structure (1-6), information (7-9), fragment (10-19), small molecular microarrays (20), and use of phenotypic assays (21-22) are selected for the studies of several DFT-based global reactivity and local reactivity descriptors to provide complete explanation for the reactivity of these complexes by chemical reactivity method. Higher HOMO-LUMO gap indicated the structural stability for the studied complexes. The complexes reflect greater thermodynamic stability. Further the results predicted that high aromaticity and hardness are measures of high stability and low reactivity for the studied complexes. It was observed that a good, more reactive, nucleophile can be described by a lower value of µ, ω while a good electrophile can be described by a high value of µ, ω. TDDFT results predicted that few complexes can be used as fluorescent biomarkers as their emission wavelength lies in the visible region. Communicated by Ramaswamy H. Sarma.

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Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

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BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. RESULTS: To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. CONCLUSIONS: Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.

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Specific structures in mRNA can stimulate programmed ribosomal frameshifting (PRF). PRF efficiency can vary enormously between different stimulatory structures, but the features that lead to efficient PRF stimulation remain uncertain. To address this question, we studied the structural dynamics of the frameshift signal from West Nile virus (WNV), which stimulates -1 PRF at very high levels and has been proposed to form several different structures, including mutually incompatible pseudoknots and a double hairpin. Using optical tweezers to apply tension to single mRNA molecules, mimicking the tension applied by the ribosome during PRF, we found that the WNV frameshift signal formed an unusually large number of different metastable structures, including all of those previously proposed. From force-extension curve measurements, we mapped 2 mutually exclusive pathways for the folding, each encompassing multiple intermediates. We identified the intermediates in each pathway from length changes and the effects of antisense oligomers blocking formation of specific contacts. Intriguingly, the number of transitions between the different conformers of the WNV frameshift signal was maximal in the range of forces applied by the ribosome during -1 PRF. Furthermore, the occupancy of the pseudoknotted conformations was far too low for static pseudoknots to account for the high levels of -1 PRF. These results support the hypothesis that conformational heterogeneity plays a key role in frameshifting and suggest that transitions between different conformers under tension are linked to efficient PRF stimulation.

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Long non-coding RNAs (lncRNAs) are key regulatory molecules, but unlike with other RNAs, the direct link between their tertiary structure motifs and their function has proven elusive. Here we report structural and functional studies of human maternally expressed gene 3 (MEG3), a tumor suppressor lncRNA that modulates the p53 response. We found that, in an evolutionary conserved region of MEG3, two distal motifs interact by base complementarity to form alternative, mutually exclusive pseudoknot structures ("kissing loops"). Mutations that disrupt these interactions impair MEG3-dependent p53 stimulation in vivo and disrupt MEG3 folding in vitro. These findings provide mechanistic insights into regulation of the p53 pathway by MEG3 and reveal how conserved motifs of tertiary structure can regulate lncRNA biological function.

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Foot-and-mouth disease virus (FMDV) is the causative agent of foot-and-mouth disease. It is characterized by genetic instability and different antigenic properties. The nonstructural protein 3A is a primary determinant of the tropism and virulence of Cathay topotype FMDVs. However, several other determinants are also speculated to be involved in viral tropism and virulence. Deletion of 43 nucleotides (nt) in the pseudoknot (PK) region of the 5' untranslated region (UTR) has been found to coexist with the identified 3A deletion in Cathay topotype FMDV genomes. In this study, we isolated an O/ME-SA/PanAsia lineage FMDV strain, O/GD/CHA/2015, that includes an 86-nt deletion in the PK region and shows a porcinophilic phenotype. To investigate the potential role of the PK region in viral pathogenicity, we generated a recombinant FMDV strain with an incomplete PK region and compared its virulence and pathogenesis to the intact FMDV strain in swine and bovines. Deletion of the 86 nt in the PKs had no major effects on the pathogenicity of the virus in swine but significantly attenuated its ability to infect bovine cells and cattle, indicating that the PK region is a newly discovered determinant of viral tropism and virulence. The role of the 43-nt deletion existing in the Cathay topotype FMDV was also investigated by evaluating the infection properties of genetically engineered viruses. Consistently, the 43-nt deletion in the PK region significantly decreased the pathogenicity of the virus in bovines. Overall, our findings suggest that the PK region deletion occurred naturally in the FMDV genome and that the PK region is highly associated with viral host range and functions as a novel determinant for FMDV pathogenesis.IMPORTANCE This study demonstrates that the deletion in the PK region occurred naturally in the FMDV genome. The isolated O/ME-SA/PanAsia lineage FMDV with an 86-nt deletion in the PK region showed a pig-adapted characteristic that could cause clinical signs in swine but not bovines. Compared to the wild-type FMDV strain, which possesses full infection capacity in both swine and bovines, the recombinant virus with the 86-nt deletion in the PK region is deficient in causing disease in bovines. Deletion of the previously reported 43 nt in the PK region also led to significantly decreased pathogenicity of FMDV in bovines. This study indicates that the PK region is a novel determinant of the tropism and virulence of FMDV.

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RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design.

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Binding of recombinant prion protein with small highly structured RNAs, prokaryotic and eukaryotic prion protein mRNA pseudoknots, tRNA and polyA has been studied by the change in fluorescence anisotropy of the intrinsic tryptophan groups of the protein. The affinities of these RNAs to the prion protein and the number of sites where the protein binds to the nucleic acids do not vary appreciably although the RNAs have very different compositions and structures. The binding parameters do not depend upon pH of the solution and show a poor co-operativity. The reactants form larger nucleoprotein complexes at pH 5 compared to that at neutral pH. The electrostatic force between the protein and nucleic acids dominates the binding interaction at neutral pH. In contrast, nucleic acid interaction with the incipient nonpolar groups exposed from the structured region of the prion protein dominates the reaction at pH 5. Prion protein of a particular species forms larger complexes with prion protein mRNA pseudoknots of the same species. The structure of the pseudoknots and not their base sequences probably dominates their interaction with prion protein. Possibilities of the conversion of the prion protein to its infectious form in the cytoplasm by nucleic acids have been discussed.

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The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA sequencing data. We then focus on the identification of a well-known class of small noncoding RNAs, namely microRNAs, which play very important roles in many biological processes through regulating post-transcriptionally the expression of genes and which dysregulation has been shown to be involved in several human diseases.

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