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

Power of the interactive proof systems with verifiers modeled by semi-quantum two-way finite automata

Use of linear regression model to compare RNA secondary structures

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.

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.

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.

Know the chemical structures of DNA and ribonucleic acid (RNA), and how these chemical structures are related to the functions of these biological macromolecules.

Ribonucleic acid (RNA) is a polymeric nucleic acid that is crucial for cellular function, regulating gene expression and encoding/decoding protein/DNA molecules. Recent discoveries of diverse functionality in non-coding RNAs have led to unprecedented demand for RNA 3D structure determination. With current technology, general, accurate prediction of 3D structures for large RNAs from the sequence remains computationally intractable. One of the principal challenges arises from the conformational flexibility of RNA, especially in loop/junction regions, which results in a rugged energy landscape. Several strategies exist to overcome this challenge, including incorporation of efficient experimental information and coarse-grained (CG) modeling to improve computational sampling of the structural ensemble. A second challenge is the inclusion of naturally modified derivatives of canonical RNA nucleotides in structure analysis. Most RNA prediction strategies rely upon the canonical nucleotides (adenine (A), uracil (U), guanine (G), and cytosine (C)), ignoring the effects of modified nucleotides on the structure and system dynamics. In general, RNA molecules contain rigid and flexible structural elements, which can be probed using efficient selective 2'-hydroxyl analyzed by primer extension (SHAPE) experiments. SHAPE experiments selectively modify flexible RNA nucleotides and can be processed to produce a characteristic reactivity profile for an RNA molecule that contains structural information. Incorporation of efficient experimental information, such as SHAPE, in predicting RNA 3D structure is highly desirable for overcoming the current knowledge gap between RNA sequence and 3D structure. In the first project, we introduce a physics-based model, the 3D structure-SHAPE relationship (3DSSR) model, to predict the SHAPE reactivity from the structure and show how this model may be used to sieve SHAPE-compatible structures from a pool of low-energy decoys and refine our predictions. In the second project, we compare 3DSSR performance to that of a convolutional neural network (CNN) trained on the SHAPE data and RNA structures, showing that 3DSSR outperforms the CNN given the limited data available. In the third project, we further improve the 3DSSR model, gaining deeper insights into the SHAPE reaction and biases. In the fourth project, we explore the theory underpinning the iterative simulated CG RNA folding model (IsRNA). In establishing the underlying mechanics driving the success of the model, we were able to clarify and improve the parameterization method while expanding the model interpretation, which should broaden application of the method to other biopolymers, such as protein. We found that the parameterization method follows statistical mechanics principles but also has a Bayesian interpretation. Further, we found that the parameterization process can benefit from application of the principle of maximum entropy, which improves simulation and parameterization efficiency. In the fifth project, we investigate the impact of nucleotide modification on the structure and configurational ensemble of RNA molecules using free energy calculations. By applying modifications to a common RNA hairpin, we estimate the impact on the stability of the structural ensemble, identifying specific interactions that drive changes to the potential of mean force (PMF) and showing the context and modification-dependence of the variable alterations to the structure stability.

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.

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

The structure of ribonucleic acid (RNA) polymers is strongly dependent on the presence of, in particular Mg2+ cations to stabilize structural features. Only in high-resolution X-ray crystallography structures can ions be identified reliably. Here, we perform molecular dynamics simulations of 24 RNA structures with varying ion concentrations. Twelve of the structures were helical and the others complex folded. The aim of the study is to predict ion positions but also to evaluate the impact of different types of ions (Na+ or Mg2+) and the ionic strength on structural stability and variations of RNA. As a general conclusion Mg2+ is found to conserve the experimental structure better than Na+ and, where experimental ion positions are available, they can be reproduced with reasonable accuracy. If a large surplus of ions is present the added electrostatic screening makes prediction of binding-sites less reproducible. Distinct differences in ion-binding between helical and complex folded structures are found. The strength of binding (ΔG‡ for breaking RNA atom-ion interactions) is found to differ between roughly 10 and 26 kJ/mol for the different RNA atoms. Differences in stability between helical and complex folded structures and of the influence of metal ions on either are discussed.

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.

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.

This chapter introduces fundamental concepts in medical genetics and genomics, focusing on nucleic acids and inheritance. It explains the structure of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), detailing how their molecular composition enables genetic information storage and expression. The chapter examines gene structure, distinguishing between coding and non-coding regions, and describes how DNA transmits genetic information across generations through inheritance patterns. It explores key molecular processes, including DNA replication, transcription, and translation, while also addressing genetic variations and their effects on gene function. Finally, the chapter discusses mitochondrial DNA, its unique inheritance patterns, and its role in human health and disease.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data