A Review of Bioinformatics Methods for RNA Secondary and Tertiary Structures Prediction

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Author(s): Abdelhakim El Fatmi* and Molay Ali Bekri

Abstract

Initially, it was thought that Ribonucleic acid (RNA) primarily served as a carrier of information required for protein synthesis. However, recent discoveries have unveiled RNA’s pivotal involvement in gene regulatory networks and various cellular processes. The functionality of an RNA molecule is intricately linked to its structures, including secondary and tertiary (three-dimensional) structures. Consequently, comprehending the functions of RNA necessitates a thorough understanding of its diverse structural aspects.

Deciphering the secondary structure or tertiary structure of RNA from its primary sequence is a complex computational challenge. Consequently, several methods and techniques have been devised to address this issue. Initially, physical techniques like X-Ray, Crystallography, and Nuclear Magnetic Resonance were employed, but they proved to be expensive, labor-intensive, and time-consuming. As a result, the demand for bioinformatics methods has significantly increased to tackle this problem more efficiently.

In this paper, we will explore the various employed methods for predicting the different RNA structures, encompassing both secondary and tertiary structures.

Introduction

Ribonucleic acid (RNA) consists of a sequence of nucleotides: Adenine (A), Cytosine (C), Guanine (G), and Uracil (U). Within the cell, RNA molecules play crucial roles in diverse biological processes. While its initial well-known function involves transporting genetic information from DNA to proteins, it has been found to perform numerous other functions. Notably, the discovery of novel RNA classes, such as non-coding RNAs, like small nuclear RNAs (snRNAs), has expanded our understanding. These snRNAs combine with proteins to form small nuclear ribonucleoproteins (snRNPs) involved in RNA splicing. In general, an RNA molecule’s function is intricately tied to its structures, particularly its secondary and tertiary structures [1-5].

When seeking molecular structures, the final step involves predicting the three-dimensional shape of the molecule. Achieving high-precision predictions, especially for large RNA molecules with only their primary structure as information, remains a formidable challenge. Conventional physical techniques like X-Ray, Crystallography, and Nuclear Magnetic Resonance are not only expensive but also demand significant effort and time [6]. Consequently, RNA structure prediction has emerged as a vital area of interest for researchers and one of the foremost challenges in the field of bioinformatics.

RNA Structures

Primary, Secondary and Tertiary Structure

The RNA molecule exhibits a hierarchical organization with three structural levels: the primary structure, secondary structure, and tertiary structure [7]. The distinct functions of RNA are often revealed by analyzing its complex structures, particularly its secondary and tertiary structures [5] (see Figure 1).

Primary structure

The primary structure of Ribonucleic acid (RNA) comprises a series of nucleotides, which include Adenine (A), Cytosine (C), Guanine (G), and Uracil (U), linked together by phosphodiester bonds. This series is commonly referred to as the RNA sequence or RNA primary structure [8] (as illustrated in Figure 1.1).

Secondary structure

The secondary structure of an RNA sequence arises when the RNA strand folds on itself, by establishing hydrogen bonds between G-C, A-U, and G-U base pairs [1] (see Figure 1.2). Predicting RNA secondary structure involves determining these hydrogen bonds in an RNA molecule solely from its primary sequence. RNA secondary structure encompasses various elements, including stacked pairs or stems, hairpin loops, multi-branched loops, internal loops, bulge loops, and a more complex components known as a pseudoknot [8].

Tertiary Structure or Three-Dimensional (3D) Structure

The RNA tertiary structure refers to the spatial or three- dimensional organization of RNA components (see Figure 1.3), encompassing helical duplexes, triple-stranded structures, and various components interconnected by a set of interactions known as RNA tertiary interactions [9].

Figure 1: The Three Levels of RNA Structure

RNA Structures Prediction Approaches and Algorithms RNA structures determination plays a vital role in addressing various challenges associated with understanding the RNA physical structure, including deciphering the three-dimensional arrangement and interpreting the biochemical functions of these molecules [5].

The utilization of experimental techniques such as Crystallography, X-Ray, and Nuclear Magnetic Resonance for RNA structure determination necessitates significant financial resources, substantial effort, and a substantial time commitment [6]. In response, bioinformaticians have introduced diverse methods and algorithms as an alternative approach to predict secondary and tertiary structures using various strategies.

In this section, we will shed light on the most commonly used approaches for predicting RNA secondary and tertiary structures.

•  RNA Secondary Structure Prediction

The process of RNA folding is typically hierarchical, with local interactions taking precedence and being energetically more significant than tertiary interactions [10-11]. As a result, the RNA secondary structure serves as a foundational framework for its native 3D structure, and it can be predicted without requiring knowledge of tertiary interactions.

Over the past three decades, numerous methods have been developed for predicting RNA secondary structure. These methods can be categorized into three groups: Dynamic Programming Approach (DPA), Soft Computing (SC), and Comparative Approach (CA). In the subsequent sections, we will offer a concise overview of these approaches.

• Dynamic Programming Approach (DPA)

Dynamic Programming (DPA) operates on the concept of breaking down a complex problem into smaller sub problems. By integrating this concept with the principle of minimizing free energy, numerous algorithms have been developed for RNA structure prediction. According to the free energy minimization principle, the structure of an RNA sequence can be the most stable one with the lowest free energy. The most fundamental dynamic programming algorithms generate basic secondary structures without pseudoknot component. Typically, these algorithms face challenges due to their significant time and space complexities [8]. Nussinov et al. introduced the initial algorithm for RNA secondary structure prediction, grounded in the concept of minimizing free energy [12]. The objective of this algorithm is to maximize the number of base pairs to achieve minimal free energy. The computational time complexity for this algorithm is O(n^{^3}). Subsequently, Zuker presented a well-known algorithm, known as Mfold, for predicting RNA secondary structures without considering pseudoknots [13]. This program forecasts secondary structures by minimizing free energy, based on the thermodynamic model proposed by Tinoco in [14].

Soft Computing

Soft Computing (SC) comprises a collection of methods that can be employed either individually or in combination to address various practical challenges. SC methodologies exploit the tolerance of imprecision, uncertainty, approximate reasoning, and partial truths in order to provide cost-effective and optimal solutions [25].

Among of the most commonly utilized SC techniques for RNA structures prediction include Evolutionary Computation, such as Genetic algorithms, and Artificial Neural Networks (ANN).

Genetic Algorithm (GA)

Genetic algorithms (GAs) are dynamic and robust tools founded on the concepts of selection and evolution. GAs are employed to generate multiple solutions for a specific problem [26]. Genetic algorithms become the preferred option when addressing real- world challenges characterized by vast and intricate search spaces, rendering conventional search methods ineffective.

The utilization of Genetic Algorithms (GAs) necessitates the definition of specific operators, including selection, crossover, and mutation. Additionally, a fitness function is essential to assess the quality of each solution. The application of GAs for predicting secondary structures has been explored in numerous studies.

Based on the free energy minimization principle and the RNA folding pathways, Van Batenburg et al. introduce a Genetic Algorithm (GA) for RNA secondary structure prediction [27]. This algorithm commences by creating a list of potential structures, which constitutes the initial population. The operations of crossover and mutation serve as key operators, and two distinct fitness criteria are applied: the summation of stem lengths and the summation of stem stacking energies.

Artificial Neural Networks (ANN)

Artificial Neural Networks (ANNs) represent an information processing system composed of a large number of interconnected processing units often referred to as “neurons.” These neurons work collaboratively to address specific problems. ANNs are designed to tackle artificial intelligence challenges by acquiring knowledge through learning. ANNs also find applications in tasks such as classification, clustering, and prediction [8]. Several studies have explored the use of ANNs for RNA structures prediction.

In reference 28, they introduce a parallel algorithm based on a Hopfield Neural Network (HNN) for the prediction of RNA structures [28]. This method employs the HNN to identify a nearly maximal independent set within an adjacent graph formed by RNA base pairs. Subsequently, it calculates the stable structure of the RNA.

Comparative Approach (CA)

Due to the inherent connection between structure and function, it is reasonable to hypothesize that sequences sharing the same functions should also exhibit similar structures [46]. The comparative approach focuses on searching for conserved regions within the sequences, for this reason, it is employed when dealing with an alignment of sequences from different species that serve the same function. In such cases, CA is deemed more pertinent compared to the dynamic approach that relies on the thermodynamic principle [8].

The initial effective algorithm employing this approach was created by Han and Kim [47]. This algorithm takes a collection of aligned sequences and conducts a phylogenetic comparison, seeking a specific number of the most likely shared structures. It comprises two key phases, with the first step dedicated to the examination of the phylogenetic comparison, while the second step is focused on choosing the most optimal secondary structures.

RNA Tertiary (3D) Structure Prediction

While the secondary structure offers a fundamental outline of an RNA molecule, having knowledge of the RNA’s 3D structure remains essential for gaining a comprehensive understanding of its function. Initially, RNA structure experts successfully constructed 3D structures for common RNA molecules like tRNAs, group II introns, and RNase P [59].

In recent years, various computational methods have been developed to predict RNA 3D structures. These methods can be categorized into three models or approaches, including Knowledge-based approach, physics-based approach, and Deep-Learning-Based approach.

Knowledge-Based Modeling

As the number of experimentally determined structures in the database continues to grow, RNA 3D structures can be predicted through the assembly of known motifs or sequence alignment. Knowledge-based modeling primarily encompasses graphic-based modeling and homology-based modeling [60].

Graphics-Based Methods

Graphical modeling typically offers a graphical interface, enabling users to create RNA 3D structures through the manipulation or assembly of RNA segments. The primary graphical-based algorithms include, ERNA-3D, MANIP, RNA2D3D, S2S/Assemble [61-64].

Homology-Based Methods

Given that the 3D structure of a macromolecule evolves at a much slower rate than its sequence, evolutionarily related macromolecules typically maintain similar 3D structures even when their sequences diverge. Leveraging this observation, the 3D structures of a macromolecule can be constructed by aligning the sequence of the target molecule with the structures of template molecules.

Comparative modeling, which is also known as homology-based modeling or template-based modeling, has proven to be quite effective in predicting the 3D structures of proteins. Similarly, in the context of RNA 3D structure prediction, several algorithms, including Mode RNA [66], RNA Builder, and 3Drna, have been created for constructing RNA 3D structures [65-68].

Physics-BasedModeling

Physics-based methods rely on biophysical principles to simulate the folding process, aiming to find a conformation with the lowest free energy. Given that modeling a full-atom RNA structure typically entails numerous degrees of freedom and, consequently, significant computational complexity, various coarse-grained predictive models have been introduced at different resolution levels, incorporating physical simplifications [59].

All-Atomistic Model

Traditionally, all-atomistic molecular dynamics using physics-based atomic force fields like CHARMM and AMBER have been widely employed for macromolecular modeling, offering insights into the actual movements of atoms [7]. However, the challenge of folding RNA 3D structures persists due to the numerous degrees of freedom, even with the most advanced computational resources [5]. Consequently, models that assemble all-atomistic fragments based on known fragments or secondary structures have been developed, as exemplified by the Ernwin, RNAnbds and RSIM [76-80].

Coarse-Grained Model

An alternative approach to reduce computational costs involves reducing the number of particles by considering a cluster of functional atoms as a single bead. The size of this bead can vary, representing either a small group of atoms or a larger assembly, depending on the model’s resolution [5]. After the initial development of the one-bead RNA model by Malhotra and Harvey, numerous coarse- grained (CG) models have been introduced to predict RNA 3D structures or to model interactions between RNAs and other molecules, examples of which include Vfold, YUP and NAST [81- 84].

TheDeep-Learning-Based Approaches

The rise of artificial intelligence has made significant strides in advancing science and technology worldwide in recent years. Building upon the success of deep learning in protein 3D structure prediction, some deep-learning-based methods have emerged for predicting RNA 3D structures, albeit facing the challenge of limited RNA structural data in the PDB database when compared to proteins [95]. Nevertheless, it is important to acknowledge that achieving precise predictions of macromolecule 3D structures often hinges on the availability of extensive experimental structural data.

Conclusion

As comprehending RNA structures, particularly in three dimensions, holds vital significance in unraveling the enigmatic RNA world, there has been a surge in the development of computational models for RNA structure modeling in recent years.

Predicting RNA secondary structures is comparatively more manageable through techniques like sequence alignment, thermodynamics-based dynamic programming algorithms, or a combination thereof. Enhancing the accuracy of such predictions can be achieved through expanding structural databases or refining empirical thermodynamic parameters. However, the field of RNA 3D structure prediction is still in its early stages and confronts several challenges. Nonetheless, recent achievements in RNA 3D structure modeling offer promising prospects for exciting advancements in RNA structure prediction in the upcoming decade.

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