H-type Pseudoknots Research Articles

RNA fold prediction by Monte Carlo in graph space and the statistical mechanics of tertiary interactions.

Using a graph representation of RNA structures, we have studied the ensembles of secondary and tertiary graphs two sets of RNA with Monte Carlo simulations. The first consisted of 91 target ribozyme and riboswitch sequences of moderate lengths (< 150 nt) having a variety of secondary, H-type pseudoknots and kissing loop interactions. The second set consisted of 71 more diverse sequences across many RNA families. Using a simple empirical energy model for tertiary interactions and only sequence information for each target as input, the simulations examined how tertiary interactions impact the statistical mechanics of the fold ensembles. The results show that the graphs proliferate enormously when tertiary interactions are possible, producing an entropic driving force for the ensemble to access folds having tertiary structures even though they are overall energetically unfavorable in the energy model. For each of the targets in the two test sets, we assessed the quality of the model and the simulations by examining how well the simulated structures were able to predict the native fold and compared the results to fold predictions from ViennaRNA. Our model generated good or excellent predictions in a large majority of the targets. Overall, this method was able to produce predictions of comparable quality to Vienna, but it outperformed Vienna for structures with H-type pseudoknots. The results suggest that while tertiary interactions are predicated on real-space contacts, their impacts on the folded structure of RNA can be captured by graph space information for sequences of moderate lengths, using a simple tertiary energy model for the loops, the base pairs and base stacks.

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA

The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction.

Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions

RNA pseudoknots are a kind of important tertiary motif, and the structures and stabilities of pseudoknots are generally critical to the biological functions of RNAs with the motifs. In this work, we have carefully refined our previously developed coarse-grained model with salt effect through involving a new coarse-grained force field and a replica-exchange Monte Carlo algorithm, and employed the model to predict structures and stabilities of complex RNA pseudoknots in ion solutions beyond minimal H-type pseudoknots. Compared with available experimental data, the newly refined model can successfully predict 3D structures from sequences for the complex RNA pseudoknots including SARS-CoV-2 programming-1 ribosomal frameshifting element and Zika virus xrRNA, and can reliably predict the thermal stabilities of RNA pseudoknots with various sequences and lengths over broad ranges of monovalent/divalent salts. In addition, for complex pseudoknots including SARS-CoV-2 frameshifting element, our analyses show that their thermally unfolding pathways are mainly dependent on the relative stabilities of unfolded intermediate states, in analogy to those of minimal H-type pseudoknots.

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops.

The accurate "base pairing" in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar's advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

Statistics of topological RNA structures.

In this paper we study properties of topological RNA structures, i.e.RNA contact structures with cross-serial interactions that are filtered by their topological genus. RNA secondary structures within this framework are topological structures having genus zero. We derive a new bivariate generating function whose singular expansion allows us to analyze the distributions of arcs, stacks, hairpin- , interior- and multi-loops. We then extend this analysis to H-type pseudoknots, kissing hairpins as well as 3-knots and compute their respective expectation values. Finally we discuss our results and put them into context with data obtained by uniform sampling structures of fixed genus.

Structural analysis of a class III preQ1 riboswitch reveals an aptamer distant from a ribosome-binding site regulated by fast dynamics

PreQ1-III riboswitches are newly identified RNA elements that control bacterial genes in response to preQ1 (7-aminomethyl-7-deazaguanine), a precursor to the essential hypermodified tRNA base queuosine. Although numerous riboswitches fold as H-type or HLout-type pseudoknots that integrate ligand-binding and regulatory sequences within a single folded domain, the preQ1-III riboswitch aptamer forms a HLout-type pseudoknot that does not appear to incorporate its ribosome-binding site (RBS). To understand how this unusual organization confers function, we determined the crystal structure of the class III preQ1 riboswitch from Faecalibacterium prausnitzii at 2.75 Å resolution. PreQ1 binds tightly (KD,app 6.5 ± 0.5 nM) between helices P1 and P2 of a three-way helical junction wherein the third helix, P4, projects orthogonally from the ligand-binding pocket, exposing its stem-loop to base pair with the 3' RBS. Biochemical analysis, computational modeling, and single-molecule FRET imaging demonstrated that preQ1 enhances P4 reorientation toward P1-P2, promoting a partially nested, H-type pseudoknot in which the RBS undergoes rapid docking (kdock ∼ 0.6 s(-1)) and undocking (kundock ∼ 1.1 s(-1)). Discovery of such dynamic conformational switching provides insight into how a riboswitch with bipartite architecture uses dynamics to modulate expression platform accessibility, thus expanding the known repertoire of gene control strategies used by regulatory RNAs.

PKscan: a program to identify H-type RNA pseudoknots in any RNA sequence with unlimited length

A computer program written in C++ has been developed which can detect all potential H-type RNA pseudoknots within any given RNA sequence. There is no limit on the length of the input sequence. A validation run of the program using the full-length (8173 nt) genomic mRNA of simian retrovirus type-1 (SRV-1) identifies the established -1 frameshift stimulating pseudokont at the gagpro junction as the most stable pseudoknot within the genomic mRNA.

A Genome-Wide Analysis of RNA Pseudoknots That Stimulate Efficient −1 Ribosomal Frameshifting or Readthrough in Animal Viruses

Programmed −1 ribosomal frameshifting (PRF) and stop codon readthrough are two translational recoding mechanisms utilized by some RNA viruses to express their structural and enzymatic proteins at a defined ratio. Efficient recoding usually requires an RNA pseudoknot located several nucleotides downstream from the recoding site. To assess the strategic importance of the recoding pseudoknots, we have carried out a large scale genome-wide analysis in which we used an in-house developed program to detect all possible H-type pseudoknots within the genomic mRNAs of 81 animal viruses. Pseudoknots are detected downstream from ~85% of the recoding sites, including many previously unknown pseudoknots. ~78% of the recoding pseudoknots are the most stable pseudoknot within the viral genomes. However, they are not as strong as some designed pseudoknots that exhibit roadblocking effect on the translating ribosome. Strong roadblocking pseudoknots are not detected within the viral genomes. These results indicate that the decoding pseudoknots have evolved to possess optimal stability for efficient recoding. We also found that the sequence at the gag-pol frameshift junction of HIV1 harbors potential elaborated pseudoknots encompassing the frameshift site. A novel mechanism is proposed for possible involvement of the elaborated pseudoknots in the HIV1 PRF event.

Using folding ensemble and stem probability maximization methods to predict RNA H-type pseudoknots

Using folding ensemble and stem probability maximization methods to predict RNA H-type pseudoknots

Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins

Pseudoknots are an essential feature of RNA tertiary structures. Simple H-type pseudoknots have been studied extensively in terms of biological functions, computational prediction, and energy models. Intramolecular kissing hairpins are a more complex and biologically important type of pseudoknot in which two hairpin loops form base pairs. They are hard to predict using free energy minimization due to high computational requirements. Heuristic methods that allow arbitrary pseudoknots strongly depend on the quality of energy parameters, which are not yet available for complex pseudoknots. We present an extension of the heuristic pseudoknot prediction algorithm DotKnot, which covers H-type pseudoknots and intramolecular kissing hairpins. Our framework allows for easy integration of advanced H-type pseudoknot energy models. For a test set of RNA sequences containing kissing hairpins and other types of pseudoknot structures, DotKnot outperforms competing methods from the literature. DotKnot is available as a web server under http://dotknot.csse.uwa.edu.au.

Predicting structures and stabilities for H-type pseudoknots with interhelix loops.

RNA pseudoknots play a critical role in RNA-related biology from the assembly of ribosome to the regulation of viral gene expression. A predictive model for pseudoknot structure and stability is essential for understanding and designing RNA structure and function. A previous statistical mechanical theory allows us to treat canonical H-type RNA pseudoknots that contain no intervening loop between the helices (see S. Cao and S.J. Chen [2006] in Nucleic Acids Research, Vol. 34; pp. 2634-2652). Biologically significant RNA pseudoknots often contain interhelix loops. Predicting the structure and stability for such more-general pseudoknots remains an unsolved problem. In the present study, we develop a predictive model for pseudoknots with interhelix loops. The model gives conformational entropy, stability, and the free-energy landscape from RNA sequences. The main features of this new model are the computation of the conformational entropy and folding free-energy base on the complete conformational ensemble and rigorous treatment for the excluded volume effects. Extensive tests for the structural predictions show overall good accuracy with average sensitivity and specificity equal to 0.91 and 0.91, respectively. The theory developed here may be a solid starting point for first-principles modeling of more complex, larger RNAs.

Pairwise Coupling Analysis of Helical Junction Hydrogen Bonding Interactions in Luteoviral RNA Pseudoknots

A 28-nucleotide mRNA pseudoknot that overlaps the P1 and P2 genes of sugarcane yellow leaf virus (ScYLV) stimulates -1 ribosomal frameshifting. The in vitro frameshifting efficiency is decreased >or=8-fold upon substitution of the 3'-most loop 2 nucleotide (C27) with adenosine, which accepts a hydrogen bond from the 2'-OH group of C14 in stem S1. The solution structures of the wild-type (WT) and C27A ScYLV RNA pseudoknots show that while the RNAs adopt virtually identical overall structures, there are significant structural differences at the helical junctions of the two RNAs. Specifically, C8(+) in loop L1 in the C8(+).(G12.C28) L1-S2 major groove base triple is displaced by approximately 2.3 A relative to the accepting stem 2 base pair (G12.C28) in the C27A RNA. Here, we use a double mutant cycle approach to analyze the pairwise coupling of the C8(+).(G12.C28)...C27.(C14-G7) and ...A27.(C14-G7) hydrogen bonds in the WT and C27A ScYLV RNAs, respectively, and compare these findings with previous results from the beet western yellows virus (BWYV) RNA. We find that the pairwise coupling free energy (delta(AB)(i)) is favorable for the WT RNA (-0.7 +/- 0.1 kcal/mol), thus revealing that formation of these two hydrogen bonds is positively cooperative. In contrast, delta(AB)(i) is 0.9 +/- 0.4 kcal/mol for the poorly functional C27A ScYLV RNA, indicative of nonadditive hydrogen bond formation. These results reveal that cooperative hydrogen bond formation across the helical stem junction in H-type pseudoknots correlates with enhanced frameshift stimulation by luteoviral mRNA pseudoknots.

A heuristic approach for detecting RNA H-type pseudoknots.

Motivation: RNA H-type pseudoknots are ubiquitous pseudoknots that are found in almost all classes of RNA and thought to play very important roles in a variety of biological processes. Detection of these RNA H-type pseudoknots can improve our understanding of RNA structures and their associated functions. However, the currently existing programs for detecting such RNA H-type pseudoknots are still time consuming and sometimes even ineffective. Therefore, efficient and effective tools for detecting the RNA H-type pseudoknots are needed. Results: In this paper, we have adopted a heuristic approach to develop a novel tool, called HPknotter, for efficiently and accurately detecting H-type pseudoknots in an RNA sequence. In addition, we have demonstrated the applicability and effectiveness of HPknotter by testing on some sequences with known H-type pseudoknots. Our approach can be easily extended and applied to other classes of more general pseudoknots. Availability: The web server of our HPknotter is available for online analysis at http://bioalgorithm.life.nctu.edu.tw/HPKNOTTER/ Contact: cllu@mail.nctu.edu.tw, chiu@cc.nctu.edu.tw

Untying the FIV Frameshifting Pseudoknot Structure by MS3D

The structure of the putative feline immunodeficiency virus (FIV) ribosomal frameshifting pseudoknot (PK) has been investigated by a mass spectrometric three-dimensional (MS3D) approach, which involves the application of established solvent-accessibility probes and chemical crosslinkers with detection by electrospray ionization (ESI) Fourier transform mass spectrometry (FTMS). Regardless of their size, probed substrates can be treated with ribonucleases and analyzed by ESI-FTMS to obtain the correct position of chemically modified nucleotides. Protection maps and distance information can be utilized to generate 3D models using the constraint satisfaction algorithm provided by MC-SYM and the energy minimization modules included in CNS. Control experiments were performed on a mutant of mouse mammary tumor virus pseudoknot (VPK), for which an NMR structure is available. Comparison between the MS3D model and the high-resolution structure provided a approximately 3A root-mean-square deviation calculated from all the atoms present in double-stranded regions. Applied to FIV-PK, the MS3D approach confirmed that the selected sequence could fold into an actual pseudoknot, supporting the sequence alignment predictions. Characteristic features of H-type pseudoknots were recognized immediately, but a putative A13-U30 pair was not observed at the stem junction, making FIV-PK resemble VPK more closely than the initially suggested simian retrovirus type-1 pseudoknot. In our model, the unpaired U30 protrudes into the medium, while the hinging A13 assumes a stacked conformation that enables the stems to form a approximately 60 degrees bend and relieve the strain caused by a short loop 1. The model provided the basis to explain the different alkylation patterns observed in the absence and presence of Mg(2+), suggesting the possible formation of a specific metal-binding site between loop 1 and stem 2. This instance illustrates how the MS3D model of FIV-PK can be utilized effectively to generate hypotheses and support functional observations in the absence of a high-resolution structure.

PSEUDOVIEWER2: Visualization of RNA pseudoknots of any type.

Visualizing RNA pseudoknot structures is computationally more difficult than depicting RNA secondary structures, because a drawing of a pseudoknot structure is a graph (and possibly a nonplanar graph) with inner cycles within the pseudoknot, and possibly outer cycles formed between the pseudoknot and other structural elements. We previously developed PSEUDOVIEWER for visualizing H-type pseudoknots. PSEUDOVIEWER2 improves on the first version in many ways: (i) PSEUDOVIEWER2 is more general because it can visualize a pseudoknot of any type, including H-type pseudoknots, as a planar graph; (ii) PSEUDOVIEWER2 computes a drawing of RNA structures much more efficiently and is an order of magnitude faster in actual running time; and (iii) PSEUDOVIEWER2 is a web-based application program. Experimental results demonstrate that PSEUDOVIEWER2 generates an aesthetically pleasing drawing of pseudoknots of any type and that the new representation offered by PSEUDOVIEWER2 ensures uniform and clear drawings, with no edge crossing, for all types of pseudoknots. The PSEUDOVIEWER2 algorithm is the first developed for the automatic drawing of RNA secondary structures, including pseudoknots of any type. PSEUDOVIEWER2 is accessible at http://wilab.inha.ac.kr/pseudoviewer2/.

New developments in structure determination of pseudoknots.

Recently, several high-resolution structures of-RNA pseudoknots have become available. Here we review the progress in this area. The majority of the structures obtained belong to the classical or H-type pseudoknot family. The most complicated pseudoknot structure elucidated so far is the Hepatitis Delta Virus ribozyme, which forms a nested double pseudoknot. In particular, the structure-function relationships of the H-type pseudoknots involved in translational frameshifting have received much attention. All molecules considered show interesting new structural motifs.

New developments in structure determination of pseudoknots

Recently, several high-resolution structures of RNA pseudoknots have become available. Here we review the progress in this area. The majority of the structures obtained belong to the classical or H-type pseudoknot family. The most complicated pseudoknot structure elucidated so far is the Hepatitis Delta Virus ribozyme, which forms a nested double pseudoknot. In particular, the structure–function relationships of the H-type pseudoknots involved in translational frameshifting have received much attention. All molecules considered show interesting new structural motifs. © 1999 John Wiley & Sons, Inc. Biopoly 48: 137–153, 1998

Predicting RNA H-type pseudoknots with the massively parallel genetic algorithm.

Using the genetic algorithm (GA) for RNA folding on a massively parallel supercomputer, MasPar MP-2 with 16,384 processors, we successfully predicted the existence of H-type pseudoknots in several sequences. The GA is applied to folding the tRNA-like 3' end of turnip yellow mosaic virus (TYMV) RNA sequence with 82 nucleotides, the 3' UTRs of satellite tobacco necrosis virus (STNV)-2 RNA sequence with 619 nucleotides and STNV-I RNA sequence with 622 nucleotides, and the bacteriophage T2, T4 and T6 gene 32 mRNA sequences with 946, 1340 and 946 nucleotides, respectively. The GA's results match the phylogenetically supported tertiary structures of these sequences.