Use of linear regression model to compare RNA secondary structures

In organisms, ribonucleic acid (RNA) plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform (DWT) and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

Over the last few years, many optimization algorithms have been developed to predict the optimal secondary structure of ribonucleic acid (RNA) with “minimum free energy” (MFE). These algorithms are either inspired by dynamic programming or by meta-heuristic techniques. RNA participates in several biological activities in the organism. These activities involve protein synthesis, understanding the functional behavior of RNA molecules, coding, decoding and gene expression, carrier of transferring genetic information, formation of protein, catalyst in biomedical reactions and structural molecule in cellular organelles, transcription, etc. Beside the said activities, the major role of RNA is in developing new drugs and understanding several diseases occurred due to genetic disorder and viruses. For the above said activities, it is required to predict the correct RNA secondary structure having minimum free energy with desired prediction accuracy. This paper presents a meta-heuristic optimization algorithm to obtain the optimal secondary structure of RNA with required functionality and requires less time than the others in the literature. The performance of the proposed algorithm is checked with different existing state-of-the-art techniques. It is found that the proposed algorithm gives better results against the other techniques.

Enumeration of RNA Hairpins and Cloverleaves

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function. Current physical methods for structure determination are time-consuming and expensive, thus the methods for the computational prediction of structure are necessary. Various algorithms that have been used for RNA structure prediction based in minimum free energy include dynamic programming (DP) and meta heuristic algorithms. One of the most recent meta heuristic algorithms is Musician's behavior-inspired harmony search (HS) algorithm that has been successful in numerous complex optimization problems. This paper builds on the previous work of the harmony search algorithm (HSRNAFold) which was used to find the RNA secondary structure with minimum free energy. In this paper, the accuracy of prediction is compared to the dynamic programming technique RNAFold. The results show that HSRNAFold is able to predict more accurate structures than RNAFold for all test sequences.

Modeling of RNA secondary structures using two-way quantum finite automata

Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures tend to yield better accuracy than predicting the secondary structure using the entire RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. The RNA sequence can be cut into chunks using different cutting methods and chunk lengths. Several prediction methods, with different degree of accuracy and computing requirements, can be used. The reconstruction of shorter predictions into the entire sequence can rely on simply gluing the parts together or on using more sophisticated merging algorithms. To allow scientists to perform a systematic analysis of the impact of the several methods and parameters, we propose a modularized framework using MapReduce. The framework enables scientists to automatically and efficiently explore large parametric spaces of chunking, prediction, reconstruction, and analysis methods. This paper shows how the MapReduce framework allows scientists to gain insights about different chunking strategies easily, accurately, and efficiently.

A parallel algorithm for estimating the secondary structure of an RNA molecule is presented in this paper. The mathematical problem to compute an optimal folding based on free-energy minimization is mapped onto a graph planarization problem. In the planarization problem we want to maximize the number of edges in a plane with no two edges crossing each other. To solve a sequence of n bases, n(n -- 1)/2 processing elements are used in our algorithm.

A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary structure in ribonucleic acids (RNA). The proposed system, composed of an n neural network array (where n is the number of edges in the circle graph of the number of possible base pairs), not only generates a near-maximum independent set but also predicts the secondary structure of ribonucleic acids within several hundred iteration steps. The simulator discovered several solutions which are more stable structures, in a sequence of 359 bases from the potato spindle tuber viroid, than previously proposed structures.

A structural ribonucleic acid (RNA) motif is a recurrent subset of nucleotide arrangements in secondary or tertiary structure. RNA motifs are represented by graphs of relations where the nodes represent the nucleotides and the edges represent structural relations. In order to identify all RNA motifs from all known secondary and tertiary structures, an incremental search algorithm was developed. Given a list of motifs of size n, the algorithm builds the motifs of size n + 1. The building of an RNA motif database, the development of RNA secondary structure prediction energy potentials, and the integration of RNA motifs in 3-D modeling are discussed.

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.

Protein Property prediction is useful in the prediction of drug discovery, vaccine design and precision medicine.The existing protein folding approaches use methods like AlphaFold, Ribonucleic Acid (RNA) fold, CONTRAfold and RNA sequence based deep learning techniques that focuses on attention based neural networks for protein folding prediction.RNAfold employs dynamic programming to predict RNA secondary structure, while CONTRAfold used conditional log linear models to predict RNA structures technique with an accuracy is 90%.The limitations of AlphaFold approach are that it requires high computational resources for dynamic proteins and nonstandard interactions.RNAfold approach is limited by a simple energy model that may overlook complex interactions and doesn't account for environ-mental conditions and CONTRAfold is dependent on diverse and accurate training data, with a risk of overfitting, potentially reducing effectiveness on novel RNA sequences.This paper proposes an integrated advanced machine learning techniques with transformer model platforms to predict protein structure with improved accuracy than existing models.By developing tailored pre training models and leveraging RNA design algorithms, the proposed framework aims to predict molecular properties and enhance drug target affinity prognostication.Drawing from diverse disciplines, including molecular biology and graph theory, the paper employs optimized deep learning architectures to optimize model training processes.A bioactivity dataset for small molecules with binding affinities, toxicity endpoints and protein targets are considered for the research work.The proposed model represents a systematic effort to harness advanced machine learning for healthcare advancement with the efficiency of 99.9%.

TRAM domain proteins present in Archaea and Bacteria have a β-barrel shape with anti-parallel β-sheets that form a nucleic acid binding surface; a structure also present in cold shock proteins (Csps). Aside from protein structures, experimental data defining the function of TRAM domains is lacking. Here, we explore the possible functional properties of a single TRAM domain protein, Ctr3 (cold-responsive TRAM domain protein 3) from the Antarctic archaeon Methanococcoides burtonii that has increased abundance during low temperature growth. Ribonucleic acid (RNA) bound by Ctr3 in vitro was determined using RNA-seq. Ctr3-bound M. burtonii RNA with a preference for transfer (t)RNA and 5S ribosomal RNA, and a potential binding motif was identified. In tRNA, the motif represented the C loop; a region that is conserved in tRNA from all domains of life and appears to be solvent exposed, potentially providing access for Ctr3 to bind. Ctr3 and Csps are structurally similar and are both inferred to function in low temperature translation. The broad representation of single TRAM domain proteins within Archaea compared with their apparent absence in Bacteria, and scarcity of Csps in Archaea but prevalence in Bacteria, suggests they represent distinct evolutionary lineages of functionally equivalent RNA-binding proteins.

Use of ribonucleic acid (RNA) interference to regulate protein expression has become an important research topic and gene therapy tool, and therefore, finding suitable vehicles for delivery of small RNAs into cells is of crucial importance. Layered double metal hydroxides such as hydrotalcite (HT) have shown great promise as nonviral vectors for transport of deoxyribose nucleic acid (DNA), proteins, and drugs into cells, but the adsorption of RNAs to these materials has been little explored. In this study, the binding of small RNAs with different lengths and levels of secondary structure to HT nanoparticles has been analyzed and compared to results obtained with small DNAs in concurrent experiments. Initial experiments established the spectrophotometric properties of HT in aqueous solutions and determined that HT particles could be readily sedimented with near 100% efficiencies. Use of RNA+HT cosedimentation experiments as well as electrophoretic mobility shift assays demonstrated strong adsorption of RNA 25mers to HT, with twofold greater binding of single-stranded RNAs relative to double-stranded molecules. Strong affinities were also observed with ssRNA and dsRNA 54mers and with more complex transfer RNA molecules. Competition binding and RNA displacement experiments indicated that RNA-HT associations were strong and were only modestly affected by the presence of high concentrations of inorganic anions.

One of polymeric biological molecules called ribonucleic acid (RNA) is involved in diverse biological roles in regulation of gene expression. Generally, secondary structures of RNA determine their functions in living organisms. In medical field, various structures of synthetic RNAs are much needed at present. Recently, an RNA design tool has been developed to identify optimal RNA sequences for the desired interacting RNAs based on Genetic Algorithm (GA). In an attempt to improve the design tool, we applied Artificial Bee Colony (ABC) algorithm, which can outperform several population-based methods including GA. To achieve this goal, we developed an ABC-based RNA design tool and employed this tool to design 6 different interacting RNA models. The design performance from ABC-based tool for all models were compared with that from the existing GA-based tool. It was found that the ABC-based tool and the GA-based tools equally perform in all 6 models. Both tools can achieve the fitness score above 0.996. Although the ABC-based tool could be used to design interacting RNA more reliably, it seems to require more design time, especially in the models with larger RNA length. Thus, with further improvement, this tool shall be a promising tool for designing synthetic RNA devices for biomedical applications.

The Zygnematales (Charophyta) contain a group-I intron (subgroupIC1) within their nuclear-encoded small subunit ribosomal DNA (SSU rDNA) coding region. This intron, which is inserted after position 1506 (relative to the SSU rDNA of Escherichia coli), is proposed to have been vertically inherited since the origin of the Zygnematales approximately 350-400 million years ago. Primary and secondary structure analyses were carried out to model group-I intron evolution in the Zygnematales. Secondary structure analyses support genetic data regarding sequence conservation within regions known to be functionally important for in vitro self-splicing of group-I introns. Comparisons of zygnematalean group-I intron secondary structures also provided some new insights into sequences that may have important roles in in vivo RNA splicing. Sequence analyses showed that sequence divergence rates and the nucleotide compositions of introns and coding regions within any one taxon varied widely, suggesting that the "1506" group-I introns and rDNA coding regions in the Zygnematales evolve independently.