Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

Mass Spectrometry of Nucleic Acid Noncovalent Complexes

Nucleic acids have been among the first targets for antitumor drugs and antibiotics. With the unveiling of new biological roles in regulation of gene expression, specific DNA and RNA structures have become very attractive targets, especially when the corresponding proteins are undruggable. Biophysical assays to assess target structure as well as ligand binding stoichiometry, affinity, specificity, and binding modes are part of the drug development process. Mass spectrometry offers unique advantages as a biophysical method owing to its ability to distinguish each stoichiometry present in a mixture. In addition, advanced mass spectrometry approaches (reactive probing, fragmentation techniques, ion mobility spectrometry, ion spectroscopy) provide more detailed information on the complexes. Here, we review the fundamentals of mass spectrometry and all its particularities when studying noncovalent nucleic acid structures, and then review what has been learned thanks to mass spectrometry on nucleic acid structures, self-assemblies (e.g., duplexes or G-quadruplexes), and their complexes with ligands.

An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots

BACKGROUND

RNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy.

RESULT

In order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase.

CONCLUSION

The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Background

Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information.

Results

We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0.

Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure.

Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets.

Conclusions

Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php.

RNA pseudoknots and the regulation of protein synthesis

RNA pseudoknots are structural elements found in almost all classes of RNA. Pseudoknots form when a single-stranded region in the loop of a hairpin base-pairs with a stretch of complementary nucleotides elsewhere in the RNA chain. This simple folding strategy is capable of generating a large number of stable three-dimensional folds that display a diverse range of highly specific functions in a variety of biological processes. The present review focuses on pseudoknots that act in the regulation of protein synthesis using cellular and viral examples to illustrate their versatility. Emphasis is placed on structurally well-defined pseudoknots that play a role in internal ribosome entry, autoregulation of initiation, ribosomal frameshifting during elongation and trans-translation.

Novel and efficient RNA secondary structure prediction using hierarchical folding

Algorithms for prediction of RNA secondary structure-the set of base pairs that form when an RNA molecule folds-are valuable to biologists who aim to understand RNA structure and function. Improving the accuracy and efficiency of prediction methods is an ongoing challenge, particularly for pseudoknotted secondary structures, in which base pairs overlap. This challenge is biologically important, since pseudoknotted structures play essential roles in functions of many RNA molecules, such as splicing and ribosomal frameshifting. State-of-the-art methods, which are based on free energy minimization, have high run-time complexity (typically Theta(n(5)) or worse), and can handle (minimize over) only limited types of pseudoknotted structures. We propose a new approach for prediction of pseudoknotted structures, motivated by the hypothesis that RNA structures fold hierarchically, with pseudoknot-free (non-overlapping) base pairs forming first, and pseudoknots forming later so as to minimize energy relative to the folded pseudoknot-free structure. Our HFold algorithm uses two-phase energy minimization to predict hierarchically formed secondary structures in O(n(3)) time, matching the complexity of the best algorithms for pseudoknot-free secondary structure prediction via energy minimization. Our algorithm can handle a wide range of biological structures, including kissing hairpins and nested kissing hairpins, which have previously required Theta(n(6)) time.

Viral RNA pseudoknots: versatile motifs in gene expression and replication

RNA pseudoknots are structural motifs in RNA that are increasingly recognized in viral and cellular RNAs. They have been shown to have a various roles in virus and cellular gene expression.

Pseudoknots are formed upon base pairing of a single-stranded region of RNA in the loop of a hairpin to a stretch of complementary nucleotides elsewhere in the RNA chain. This simple folding strategy can generate a large number of stable three-dimensional folds, which display a diverse range of highly specific functions.

Pseudoknot function is frequently associated with interactions with ribosomes. The inclusion of pseudoknots in an mRNA can thus confer unusual translational properties.

Many RNA viruses use pseudoknots in the control of viral RNA translation, replication and the switch between the two processes. Some satellite viruses encode ribozymes with active sites that are folded by a pseudoknot.

In cellular RNAs, pseudoknots are associated with all aspects of mRNA function and also ribosome function, as ribosomal RNAs contain numerous pseudoknots. Other essential cellular pseudoknots have been described in telomerase RNA and transfer messenger RNA.

Future research into pseudoknots will focus on structure–function relationships and bioinformatics identification of pseudoknots in genomes. The use of pseudoknots in antiviral applications could also become more widespread.

RNA pseudoknots have been identified in many different viral and cellular RNAs and are known to have various roles in virus and cellular gene expression. Here, Ian Brierley and colleagues review viral pseudoknots and the role of these structural motifs in virus gene expression and genome replication.

RNA pseudoknots are structural elements found in almost all classes of RNA. First recognized in the genomes of plant viruses, they are now established as a widespread motif with diverse functions in various biological processes. This Review focuses on viral pseudoknots and their role in virus gene expression and genome replication. Although emphasis is placed on those well defined pseudoknots that are involved in unusual mechanisms of viral translational initiation and elongation, the broader roles of pseudoknots are also discussed, including comparisons with relevant cellular counterparts. The relationship between RNA pseudoknot structure and function is also addressed.

Parsing nucleic acid pseudoknotted secondary structure: algorithm and applications

Accurate prediction of pseudoknotted nucleic acid secondary structure is an important computational challenge. Prediction algorithms based on dynamic programming aim to find a structure with minimum free energy according to some thermodynamic ("sum of loop energies") model that is implicit in the recurrences of the algorithm. However, a clear definition of what exactly are the loops in pseudoknotted structures, and their associated energies, has been lacking. In this work, we present a complete classification of loops in pseudoknotted nucleic secondary structures, and describe the Rivas and Eddy and other energy models as sum-of-loops energy models. We give a linear time algorithm for parsing a pseudoknotted secondary structure into its component loops. We give two applications of our parsing algorithm. The first is a linear time algorithm to calculate the free energy of a pseudoknotted secondary structure. This is useful for heuristic prediction algorithms, which are widely used since (pseudoknotted) RNA secondary structure prediction is NP-hard. The second application is a linear time algorithm to test the generality of the dynamic programming algorithm of Akutsu for secondary structure prediction. Together with previous work, we use this algorithm to compare the generality of state-of-the-art algorithms on real biological structures.

Statistical thermodynamics for chain molecules with simple RNA tertiary contacts

A statistical thermodynamic model is developed for chain molecules with simple RNA tertiary contacts. The model, which accounts for the excluded volume effect and the nonadditivity in the free energy, enables reliable predictions for the conformational entropy and partition function for simple tertiary folds. Illustrative applications are made to conformational transitions involving simple tertiary contacts. The model can predict the interplay between the secondary and the tertiary interactions in the conformational changes. Though the present form of the theory is tested and validated in a two-dimensional lattice model, the methodology, which is developed based on a general graphical representation for chain conformations, is applicable to any off-lattice chain representations. Moreover, the analytical formulation of the method makes possible the systematic development of the theory for more complex tertiary structures.

A memory-efficient algorithm for multiple sequence alignment with constraints

MOTIVATION

Recently, the concept of the constrained sequence alignment was proposed to incorporate the knowledge of biologists about structures/functionalities/consensuses of their datasets into sequence alignment such that the user-specified residues/nucleotides are aligned together in the computed alignment. The currently developed programs use the so-called progressive approach to efficiently obtain a constrained alignment of several sequences. However, the kernels of these programs, the dynamic programming algorithms for computing an optimal constrained alignment between two sequences, run in (gamman2) memory, where gamma is the number of the constraints and n is the maximum of the lengths of sequences. As a result, such a high memory requirement limits the overall programs to align short sequences only.

RESULTS

We adopt the divide-and-conquer approach to design a memory-efficient algorithm for computing an optimal constrained alignment between two sequences, which greatly reduces the memory requirement of the dynamic programming approaches at the expense of a small constant factor in CPU time. This new algorithm consumes only O(alphan) space, where alpha is the sum of the lengths of constraints and usually alpha << n in practical applications. Based on this algorithm, we have developed a memory-efficient tool for multiple sequence alignment with constraints.

AVAILABILITY

http://genome.life.nctu.edu.tw/MUSICME.

HIV-1 reverse transcriptase-pseudoknot RNA aptamer interaction has a binding affinity in the low picomolar range coupled with high specificity

Systematic evolution of ligands by exponential enrichment (SELEX) is a powerful method for the identification of small oligonucleotides that bind with high affinity and specificity to target proteins. Such DNAs/RNAs are a new class of potential chemotherapeutics that could block the enzymatic activity of pathologically relevant proteins. We have conducted a detailed biochemical study of the interaction of human immunodeficiency virus 1 (HIV-1) reverse transcriptase (RT) with a SELEX-derived pseudoknot RNA aptamer. Electron paramagnetic resonance spectroscopy of site-directed spin-labeled RT mutants revealed that this aptamer was selected for binding to the "closed" conformation of the enzyme. Kinetic analysis showed that the RNA inhibitor bound to HIV RT in a two-step process, with association rates similar to those described for model DNA/DNA and DNA/RNA substrates. However, the dissociation of the pseudoknot RNA from RT was dramatically slower than observed for model substrates. Equilibrium binding studies revealed an extraordinarily low K(d), of about 25 pm, for the enzyme-aptamer interaction, presumably a consequence of the slow off-rates. Additionally, this pseudoknot aptamer is highly specific for HIV-1 RT, with the closely related HIV-2 enzyme showing a binding affinity close to 4 orders of magnitude lower.