Structure Of Ribonucleic Acid Research Articles

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

RnaCrosslinkOO: an object-oriented R package for the analysis of RNA structural data generated by RNA crosslinking experiments.

RNA (ribonucleic acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localization. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analyzing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction. rnaCrosslinkOO is freely available to noncommercial users and implemented in R, with the source code and documentation accessible at https://CRAN.R-project.org/package=rnaCrosslinkOO. The software is supported on Linux, macOS, and Windows platforms.

Covalent Organic Frameworks as Nano-Reservoir for Room Temperature RNA Storage.

The emerging role of Ribonucleic acids (RNAs) as therapeutics is alluring. However, RNAs are extremely labile under ambient conditions and typically need to be stored in cryogenic conditions (-20 °C to -80 °C). Hence, storage, stabilization, and transportation of RNA under ambient conditions have been an arduous task and remain an unsolved problem. In this work, a guanidinium-based ionic covalent organic framework (COF), TTGCl with nanotubular morphology, was synthesized and used as nano-reservoirs for room-temperature storage of RNA. To understand the role of the nanotubular morphology and chemical nature of TTGCl in stabilizing the RNA structure and for comparison purposes, a neutral COF, TMT-TT, is synthesized and studied. Further, density functional theory (DFT) studies confirmed non-covalent interaction between the COFs and the RNA nucleobases, facilitating reversible storage of RNA. RNA loaded in COFs was found to be resistant to enzymatic degradation when treated with RNase. Gel electrophoresis and sequencing confirmed the structural integrity of the recovered RNAs and their further processibility.

Optimized RNA structure alignment algorithm based on longest arc-preserving common subsequence

<abstract> <p>Ribonucleic acid (RNA) structure alignment is an important problem in computational biology to identify structural similarity of RNAs. Obtaining an efficient method for this problem is challenging due to the high computational time for the optimal solution and the low accuracy of a heuristic solution. In this paper, an efficient algorithm is proposed based on a mathematical model called longest arc-preserving common subsequence. The proposed algorithm uses a heuristic technique and high-performance computing to optimize the solution of RNA structure alignment, both in terms of the running time and the accuracy of the output. Extensive experimental studies on a multicore system are conducted to show the effectiveness of the proposed algorithm on two types of data. The first is simulated data that consists of 450 comparisons of RNA structures, while the second is real biological data that consists of 357 comparisons of RNA structures. The results show that the proposed algorithm outperforms the best-known heuristic algorithm in terms of execution time, with a percentage improvement of 71% and increasing the length of the output, i.e., accuracy, by approximately 45% in all studied cases. Finally, future approaches are discussed.</p> </abstract>

Graf Çekirdek ve Graf Sinir Ağı Yöntemlerini Kullanarak RNA Moleküllerini Sınıflandırılmak İçin 3D RNA Graf Temsili Yöntemleri

Ribonucleic acids (RNAs) are nucleic acid types with 1D/2D/3D structural shapes and are essential for sustaining life. These structural shapes of the RNAs are highly correlated with their functions. While the primary and secondary structures of RNA have been extensively studied, the tertiary structure has received relatively less attention. In this article, we present novel approaches for representing 3D RNA structures as graph data, employing geometric measurements such as Base position, Square root velocity function (SRVF), Arc length, and Curvature. Then, we utilise kernel methods and neural network methods to predict RNA functions. Our findings demonstrate the effectiveness of these methodologies in unraveling the functional attributes of RNA molecules, thus enriching our understanding of their complex biological significance.

Recent Advances in RNA-Targeted Cancer Therapy.

Ribonucleic acid (RNA) plays a pivotal role in gene regulation and protein biosynthesis. Interfering the physiological function of key RNAs to induce cell apoptosis holds great promise for cancer treatment. Many RNA-targeted anti-cancer strategies have emerged continuously. Among them, RNA interference (RNAi) has been recognized as a promising therapeutic modality for various disease treatments. Nevertheless, the primary obstacle in siRNA delivery-escaping the endosome and crossing the plasma membrane severely impedes its therapeutic potential. Thus far, a variety of nanosystems as well as carrier-free bioconjugation for siRNA delivery have been developed and employed to enhance the drug delivery and anti-tumor efficiency. Besides, the use of small molecules to target specific RNA structures and disrupt their function, along with the covalent modification of RNA, has also drawn tremendous attention recently owing to high therapeutic efficacy. In this review, we will provide an overview of recent progress in RNA-targeted cancer therapy including various siRNA delivery strategies, RNA-targeting small molecules, and newly emerged covalent RNA modification. Finally, challenges and future perspectives faced in this research field will be discussed.

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints.

Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.

RNA 3D Structure Prediction: Progress and Perspective.

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

ABSTRACT Ribonucleic acids (RNA) are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy (MFE) of RNA molecules' secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods (All Paths Cycle Embeddings (APC), Shortest Paths Kernel/Embedding (SP), and Weisfeiler - Lehman and Optimal Assignment Kernel (WLOA)) on our dataset [1] using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm (Needleman-Wunsch) used for DNA and RNA comparison.

Scaling properties of RNA as a randomly branching polymer.

Formation of base pairs between the nucleotides of a ribonucleic acid (RNA) sequence gives rise to a complex and often highly branched RNA structure. While numerous studies have demonstrated the functional importance of the high degree of RNA branching-for instance, for its spatial compactness or interaction with other biological macromolecules-RNA branching topology remains largely unexplored. Here, we use the theory of randomly branching polymers to explore the scaling properties of RNAs by mapping their secondary structures onto planar tree graphs. Focusing on random RNA sequences of varying lengths, we determine the two scaling exponents related to their topology of branching. Our results indicate that ensembles of RNA secondary structures are characterized by annealed random branching and scale similarly to self-avoiding trees in three dimensions. We further show that the obtained scaling exponents are robust upon changes in nucleotide composition, tree topology, and folding energy parameters. Finally, in order to apply the theory of branching polymers to biological RNAs, whose length cannot be arbitrarily varied, we demonstrate how both scaling exponents can be obtained from distributions of the related topological quantities of individual RNA molecules with fixed length. In this way, we establish a framework to study the branching properties of RNA and compare them to other known classes of branched polymers. By understanding the scaling properties of RNA related to its branching structure, we aim to improve our understanding of the underlying principles and open up the possibility to design RNA sequences with desired topological properties.

P-FARFAR2: A multithreaded greedy approach to sampling low-energy RNA structures in Rosetta FARFAR2

RNA (ribonucleic acid) structure prediction finds many applications in health science and drug discovery due to its importance in several life regulatory processes. But despite significant advances in the close field of protein prediction, RNA 3D structure still poses a tremendous challenge to predict, especially for large sequences. In this regard, the approach unfolded by Rosetta FARFAR2 (Fragment Assembly of RNA with Full-Atom Refinement, version 2) has shown promising results, but the algorithm is non-deterministic by nature. In this paper, we develop P-FARFAR2: a parallel enhancement of FARFAR2 that increases its ability to assemble low-energy structures via multithreaded exploration of random configurations in a greedy manner. This strategy, appearing in the literature under the term “parallel mechanism”, is made viable through two measures: first, the synchronization window is coarsened to several Monte Carlo cycles; second, all but one of the threads are differentiated as auxiliary and set to perform a weakened version of the problem. Following empirical analysis on a diverse range of RNA structures, we report achieving statistical significance in lowering the energy levels of ensuing samples. And consequently, despite the moderate-to-weak correlation between energy levels and prediction accuracy, this achievement happens to propagate to accuracy measurements.

BpRNA-align: improved RNA secondary structure global alignment for comparing and clustering RNA structures.

Ribonucleic acid (RNA) is a polymeric molecule that is fundamental to biological processes, with structure being more highly conserved than primary sequence and often key to its function. Advances in RNA structure characterization have resulted in an increase in the number of accurate secondary structures. The task of uncovering common RNA structural motifs with a collective function through structural comparison, providing a level of similarity, remains challenging and could be used to improve RNA secondary structure databases and discover new RNA families. In this work, we present a novel secondary structure alignment method, bpRNA-align. bpRNA-align is a customized global structural alignment method, utilizing an inverted (gap extend costs more than gap open) and context-specific affine gap penalty along with a structural, feature-specific substitution matrix to provide similarity scores. We evaluate our similarity scores in comparison to other methods, using affinity propagation clustering, applied to a benchmarking data set of known structure types. bpRNA-align shows improvement in clustering performance over a broad range of structure types.

Comparative analysis of mitochondrial genomes among the family Peltoperlidae (Plecoptera: Systellognatha) and phylogenetic implications

Nowadays, the position of Peltoperlidae in Systellognatha has been resolved based on morphological analyses. However, there are different opinions based on molecular data. To date, only three peltoperlid mitogenomes are available, and more sampling is needed to obtain precise phylogenetic relationships. In this study, we obtained the complete mitogenomes of Cryptoperla kawasawai (15,832 bp) and Peltoperlopsis sagittata (15,756 bp). Our results show that gene content, gene order, DmTTF binding site, nucleotide composition, codon usage, ribonucleic acid (RNA) structure, and structural elements in the control region are highly conserved in peltoperlids. Heatmap analysis of codon usage shows that the AT-rich codons UUA, AUU, UUU, and AUA were commonly used codons in the Peltoperlidae. Evolutionary rate analyses of protein-coding genes reveal that different genes have been subject to different rates of molecular evolution correlated with the GC content. All tRNA genes in peltoperlid mitogenomes have a canonical cloverleaf secondary structure except for trnS1, whose dihydrouridine arm simply forms a loop. The control region of the family has several distinct structural characteristics and has the potential to serve as effective phylogenetic markers. Phylogenetic analyses support the monophyly of Perloidea, but the monophyly of Pteronarcyoidea is still not supported. The Peltoperlidae is placed as the earliest branch within the Systellognatha, and the estimated phylogenetic relationship is: Peltoperlidae + {(Styloperlidae + Pteronarcyidae) + [Perlidae + (Chloroperlidae + Perlodidae)]}. Our results provide new insight into the phylogeny of this group.

RNA-based vaccines – types, strategies of delivery and overview of RNA vaccines


 The discovery of mRNA by Sydney Brenner dates back to 1961, but the in vivo expression of mRNA was successful only in 1990, which initiated the development of vaccines based on this molecule. During Sars-CoV-2 pandemy the interest in the use of nucleic acids in the production of drugs and vaccines has increased significantly. The success of mRNA vaccines against Sars-CoV-2 has particularly empowered the pharmaceutical industry to create newer and newer generation products based on RNA modification that could help not only in Covid-2019, but also in the prevention and treatment of other infectious diseases. RNA has a very high potential - it can be used in highly personalized therapies, furthermore the production of mRNA is cheaper, faster than the current therapeutics and the process of mRNA making is more flexible due to the great ease of producing mRNA in the process of transcription. Modifying of the structure of ribonucleic acid and the methods of its delivery leads to the creation of newer and newer vaccines. In this review, we present the potential of RNA molecule in producing vaccines, types of RNA vaccines, strategies of RNA delivery and review of existing RNA-based vaccines.

Michael Waterman's Contributions to Computational Biology and Bioinformatics.

On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).

Advancements in 3WJ-based RNA nanotechnology and its application for cancer diagnosis and therapy.

This attractive and intriguing Ribonucleic acid (RNA) nanotechnology has been conceptualized over the last two decades and with our increasing understanding of RNA structure and function and improvements of RNA nanotechnology it is now possible to use this in clinical settings. Here we review the unique properties and the recent advances in RNA nanotechnology and then look at its scientific and preclinical applications for tumor diagnosis and targeted delivery and RNA-based therapy using RNA nanoparticles with diverse structures and functions. Finally, we discuss the future perspectives and challenges to RNA nanotechnology. RNA can be designed and manipulated in a similar way to DNA while having different rules for base-pairing and displaying functions similar to proteins. Rationally designed RNA nanoparticles based on the three-way junction (3WJ) motif as the core scaffold have been extensively explored in the field of nanomedicine and targeted cancer diagnosis and therapy. RNA nanostructures based on 3WJs demonstrate promising future applications due to their thermal stability, molecular-level plasticity, multifunctional chemotherapeutic drug delivery and other intrinsic characteristics, which will greatly improve the treatment of cancer and promote further major breakthroughs in this field.

Scattering of X-ray Ultrashort Pulses by Complex Polyatomic Structures.

The scattering of X-ray ultrashort pulses (USPs) is an important aspect of the diffraction analysis of matter using modern USP sources. The theoretical basis, which considers the specifics of the interaction of ultrashort pulses with complex polyatomic structures, is currently not well developed. In general, research is focused on the specifics of the interaction of ultrashort pulses with simple systems—these are atoms and simple molecules. In this work, a theory of scattering of X-ray ultrashort pulses by complex polyatomic structures is developed, considering the specifics of the interaction of ultrashort pulses with such a substance. The obtained expressions have a rather simple analytical form, which allows them to be used in diffraction analysis. As an example, it is shown that the obtained expressions can be used to study the structures of deoxyribonucleic (DNA) and ribonucleic (RNA) acids.

Genetic association and epistatic interaction analysis of cluster of differentiation 14 and mannan-binding lectin 2 gene polymorphic variants in susceptibility to chronic periodontitis

ObjectiveTo study the association and epistatic interactions of cluster of differentiation 14 (CD14) and mannan-binding lectin 2 (MBL2) gene polymorphic variants in Indian patients with chronic periodontitis (CP). DesignWe enrolled a total of 242 individuals (121 patients and 121 control subjects), age 35 to 60 years both irrespective of gender and identified CD14 (−159C > T, NC_000005.10:g.2569190:C > T) and MBL2 (codon 52, C > T, NM_000242:c.52C > T) polymorphic variants in peripheral blood samples. We also performed epistatic interaction analysis using multifactor dimensionality reduction (MDR) approach and predicted ribonucleic acid (RNA) structure of MBL2 gene using Genebee online RNA tool. ResultsSignificantly increased frequency of ‘CT' heterozygote and variant allele ‘T' in chronic periodontitis patients was observed for both CD14 and MBL2 gene polymorphisms. MDR analysis showed approximately two-fold increased risk of CP. In silico analysis showed lack of transcription factor ETF (TEA domain family member 2) binding-site in presence of ‘T' allele ‘in CD14 (-159C > T) polymorphism. The secondary RNA structure prediction of MBL2 (codon 52, C > T) polymorphism showed structural variations having approximately similar free energies. ConclusionsA dominant effect of genotype ‘CT' heterozygote and variant ‘T' allele observed for both CD14 and MBL2 gene polymorphisms in patients with CP. The presence of ‘T' allele also results in lack of transcription factor binding site at CD14 (-159C > T) and changes in arrangements of RNA molecules that may further affect expression of CD14 and MBL2 genes leading to increased susceptibility to CP pathogenesis.

RNA 3D Structure Prediction Using Coarse-Grained Models.

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

Mining for Ligandable Cavities in RNA.

Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.