SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

StemP: A Fast and Deterministic Stem-Graph Approach for RNA Secondary Structure Prediction

We propose a new deterministic methodology to predict the secondary structure of RNA sequences. What information of stem is important for structure prediction, and is it enough ? The proposed simple deterministic algorithm uses minimum stem length, Stem-Loop score, and co-existence of stems, to give good structure predictions for short RNA and tRNA sequences. The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for structure prediction. Stem-Loop score adds structure information and speeds up the computation. The proposed method can predict secondary structure even with pseudo knots. One of the strengths of this approach is the simplicity and flexibility of the algorithm, and it gives a deterministic answer. Numerical experiments are done on various sequences from Protein Data Bank and the Gutell Lab using a laptop and results take only a few seconds.

Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies

INTRODUCTION

The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approved risdiplam, the biopharmaceutical industry is gaining confidence in the field. This review, based on the literature obtained from PubMed, aims to disseminate information about the various technologies developed for targeting RNA with small molecules and propose areas for improvement to develop drugs more efficiently, particularly those linked to diseases with unmet medical needs.

AREAS COVERED

The technologies for the identification of RNA targets, screening of chemical libraries against RNA, assessing the bioactivity and target engagement of the hit compounds, structure determination, and hit-to-lead optimization are reviewed. Along with the description of the technologies, their strengths, limitations, and examples of how they can impact drug discovery are provided.

EXPERT OPINION

Many existing technologies employed for protein targets have been repurposed for use in the discovery of RNA-targeted small molecules. In addition, technologies tailored for RNA targets have been developed. Nevertheless, more improvements are necessary, such as artificial intelligence to dissect important RNA structures and RNA-small-molecule interactions and more powerful chemical probing and structure prediction techniques.

Prediction of RNA Secondary Structure Using Quantum-inspired Genetic Algorithms

Background:

The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory.

Objective:

Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures.

Methods:

The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations.

Results:

The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity.

Conclusion:

This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.

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.

Improved Nucleic Acid Therapy with Advanced Nanoscale Biotechnology

Due to a series of systemic and intracellular obstacles in nucleic acid (NA) therapy, including fast degradation in blood, renal clearance, poor cellular uptake, and inefficient endosomal escape, NAs may need delivery methods to transport to the cell nucleus or cytosol to be effective. Advanced nanoscale biotechnology-associated strategies, such as controlling the particle size, charge, drug loading, response to environmental signals, or other physical/chemical properties of delivery carriers, have provided great help for the in vivo and in vitro delivery of NA therapeutics. In this review, we introduce the characteristics of different NA modalities and illustrate how advanced nanoscale biotechnology assists NA therapy. The specific features and challenges of various nanocarriers in clinical and preclinical studies are summarized and discussed. With the help of advanced nanoscale biotechnology, some of the major barriers to the development of NA therapy will eventually be overcome in the near future.

A Novel Efficient Simulated Annealing Algorithm for the RNA Secondary Structure Predicting with Pseudoknots

The pseudoknot structure of RNA molecular plays an important role in cell function. However, existing algorithms cannot predict pseudoknots structure efficiently. In this paper, we propose a novel simulated annealing algorithm to predict nucleic acid secondary structure with pseudoknots. Firstly, all possible maximum successive complementary base pairs would be identified and maintained. Secondary, the new neighboring state could be generated by choosing one of these successive base pairs randomly. Thirdly, the annealing schedule is selected to systematically decrease the temperature as the algorithm proceeds, the final solution is the structure with minimum free energy. Furthermore, the performance of our algorithm is evaluated by the instances from PseudoBase database, and compared with state-of-the-art algorithms. The comparison results show that our algorithm is more accurate and competitive with higher sensitivity and specificity indicators.

An RNA secondary structure prediction method based on minimum and suboptimal free energy structures

The function of an RNA-molecule is mainly determined by its tertiary structures. And its secondary structure is an important determinant of its tertiary structure. The comparative methods usually give better results than the single-sequence methods. Based on minimum and suboptimal free energy structures, the paper presents a novel method for predicting conserved secondary structure of a group of related RNAs. In the method, the information from the known RNA structures is used as training data in a SVM (Support Vector Machine) classifier. Our method has been tested on the benchmark dataset given by Puton et al. The results show that the average sensitivity of our method is higher than that of other comparative methods such as CentroidAlifold, MXScrana, RNAalifold, and TurboFold.

RNA secondary structure prediction using soft computing

Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

Introducing an RNA editing requirement into a plastid-localised transgene reduces but does not eliminate functional gene transfer to the nucleus

In higher plants, DNA transfer from the plastid (chloroplast) genome to the nucleus is a frequent, ongoing process. However, there has been uncertainty over whether this transfer occurs by a direct DNA mechanism or whether RNA intermediates are involved. Previous experiments utilising transplastomic Nicotiana tabacum (tp7 and tp17) enabled the detection of plastid-to-nucleus transfer in real time. To determine whether RNA intermediates are involved in this transfer, transplastomic lines (tpneoACG) were generated containing, in their plastid genomes, a nuclear promoter-driven kanamycin resistance gene (neo) with a start codon that required plastid RNA editing but otherwise identical to tp7 and tp17. Therefore it was expected that kanamycin resistance would only be acquired following RNA-mediated transfer of neo to the nucleus. Screening of tpneoACG progeny revealed several kanamycin-resistant plants, each of which contained the neo gene located in the nucleus. Surprisingly, neo was unedited in all these plants, indicating that neoACG was active in the absence of an edited start codon and suggesting that RNA intermediates were not involved in the transfers. However, analysis of tpneoACG revealed that only a low proportion of transcripts potentially able to mediate neo transfer were edited, thus precluding unequivocal conclusions regarding the role of RNA in plastid-to-nucleus transfer. The low proportion of edited transcripts was found to be due to predominant antisense neo transcripts, rather than to low editing efficiency of the sense transcripts. This study highlights a number of important considerations in the design of experiments utilising plastid RNA editing. The results also suggest that RNA editing sites reduce but do not eliminate functional plastid-to-nucleus gene transfer. This is relevant both in an evolutionary context and in placing RNA editing-dependent genes in the plastid genome as a means of transgene containment.