A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

The determination of RNA secondary structure (RSS) could help understand RNA's functional mechanisms, guiding the design of RNA-based therapeutics, and advancing synthetic biology applications. However, traditional methods such as NMR for determining RSS are typically time-consuming and labor-intensive. As a result, the accurate prediction of RSS remains a fundamental yet unmet need in RNA research. Various deep learning (DL)-based methods achieved improved accuracy over thermodynamic-based methods. However, the over-parameterization nature of DL makes these methods prone to overfitting and thus limits their generalizability. Meanwhile, the inconsistency of RSS predictions between these methods further aggravated the crisis of generalizability. Here, we propose TrioFold to achieve enhanced generalizability of RSS prediction by integrating base-pairing clues learned from both thermodynamic- and DL-based methods by ensemble learning and convolutional block attention mechanism. TrioFold achieves higher accuracy in intra-family predictions and enhanced generalizability in inter-family and cross-RNA-types predictions. Additionally, we have developed an online webserver equipped with widely used RSS prediction algorithms and analysis tools, providing an accessible platform for the RNA research community. This study demonstrated new opportunities to improve generalizability for RSS predictions by efficient ensemble learning of base-pairing clues learned from both thermodynamic- and DL-based algorithms.

RNA structure is hierarchical. Secondary structure contacts, i.e. the canonical base pair contacts, are generally stronger and form faster than the tertiary structure. Therefore, RNA secondary structures can be predicted independently of tertiary structure prediction. Furthermore, the stability of a given RNA secondary structure can be quantified using nearest neighbor free energy parameters. These parameters are the basis of a number of free energy minimization algorithms that predict RNA secondary structure for either a single sequence or multiple sequences. This article reviews the progress of RNA secondary structure prediction by free energy minimization and describes many of the algorithms that have been developed.

An Interactive Framework for RNA Secondary Structure Prediction with a Dynamical Treatment of Constraints

The structure of RNA is very important in biological processes. Over the recent years, lots of machine learning method have been emerged to predict the secondary structure of RNA. In this paper, we use Support Vector Machine to predict secondary structure of RNA sequence. Meanwhile, a sequence-based method is proposed by combining a new feature representation which is based on RNA long-range interaction. We first quote E-NSSEL labels to represent the secondary structure of RNA. Combining with the definition of a new feature vector based on long-range interaction, the secondary structure of test sequence is predicted by SVM model, and the corresponding E-NSSEL sequence is consequently obtained. This sequence can be restored to secondary structure finally. The results which are obtained from RNA training and testing datasets show that this long-range-sequence-based method is superior to those method without new feature. It has higher prediction accuracy as considering the new feature. Moreover, it can predict RNA sequences with long length, which is difficult to deal with traditional folding prediction. Furthermore, it suggests that our method may provide a reliable tool for RNA secondary structure prediction, including the prediction of RNA with pseudoknots.

Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA secondary structure including pseudoknot, which has been identified to be an NP-complete problem. In this paper, a novel multimodal optimization evolutionary algorithm is proposed to optimize the decision space based on the minimum free energy to predict the secondary structure of RNA. Because there exist multiple equivalent secondary structures which represent the same minimum free energy, our algorithm maintain diversity in decision space to find multiple sets of secondary structure simultaneously. The performance of our algorithm is evaluated by PseudoBase instances and compared with some good prediction algorithms. The comparison results show that our method has higher accuracy in RNA secondary structure prediction.

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Article ML-Based RNA Secondary Structure Prediction Methods: A Survey Qi Zhao 1, Jingjing Chen 1, Zheng Zhao 2, Qian Mao 3, Haoxuan Shi 1 and Xiaoya Fan 4,∗ 1 School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110000, China 2 School of Artificial Intelligence, Dalian Maritime University, Dalian 116000, China 3 Department of Food Science and Engineering, College of Light Industry, Liaoning University, Shenyang 110000, China 4 School of Software, Dalian University of Technology, Key Laboratory for Ubiquitous Network and Service Software, Dalian 116000, China ∗ Correspondence: xiaoyafan@dlut.edu.cn Received: 6 May 2024; Revised: 17 October 2024; Accepted: 22 October 2024; Published: 29 October 2024 Abstract: The secondary structure of noncoding RNAs (ncRNA) is significantly related to their functions, emphasizing the importance and value of identifying ncRNA secondary structure. Computational prediction methods have been widely used in this field. However, the performance of existing computational methods has plateaued in recent years despite various advancements. Fortunately, the emergence of machine learning, particularly deep learning, has brought new hope to this field. In this review, we present a comprehensive overview of machine learning-based methods for predicting RNA secondary structures, with a particular emphasis on deep learning approaches. Additionally, we discuss the current challenges and prospects in RNA secondary structure prediction.

RNA Secondary Structure Prediction (RSSP) is an optimization problem, where a stable secondary structure is acquired from an RNA primary sequence. Many exact, heuristic and metaheuristic algorithms established in recent years to solve the RSSP problem. We have resolved an accession based on metaheuristic algorithm named Fruit Fly Optimization algorithm to solve the RNA secondary structure prediction problem. FOA is a population-based metaheuristic that works better than all other related existing algorithms and has been employed in different optimization problems. We have redesigned the operators of the FOA algorithm and calculated the minimum Gibbs free energy (ΔG) of the structure to solve the RNA secondary structure problem. We have a Repair function which is known as novel operator that is used to verify and expel the repeated stem from RNA sequence, which is very time-efficient. Every quality of the solutions and spending time are calculated in designing the operators and the repair function. The raised methodology gives efficiency, robustness, and effectiveness in solving the RSSP problem.

To better elucidate RNA structure-function relationships and to improve the design of pharmaceutical agents that target specific RNA motifs, an understanding of RNA primary, secondary, and tertiary structure is necessary. The prediction of RNA secondary structure from sequence is an intermediate step in predicting RNA three-dimensional structure. RNA secondary structure is typically predicted using a nearest neighbor model based on free energy parameters. The current free energy parameters for 2 × 3 nucleotide loops are based on a 23-member data set of 2 × 3 loops and internal loops of other sizes. A database of representative RNA secondary structures was searched to identify 2 × 3 nucleotide loops that occur in nature. Seventeen of the most frequent 2 × 3 nucleotide loops in this database were studied by optical melting experiments. Fifteen of these loops melted in a two-state manner, and the associated experimental ΔG°(37,2×3) values are, on average, 0.6 and 0.7 kcal/mol different from the values predicted for these internal loops using the predictive models proposed by Lu, Turner, and Mathews [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924] and Chen and Turner [Chen, G., and Turner, D. H. (2006) Biochemistry 45, 4025-4043], respectively. These new ΔG°(37,2×3) values can be used to update the current algorithms that predict secondary structure from sequence. To improve free energy calculations for duplexes containing 2 × 3 nucleotide loops that still do not have experimentally determined free energy contributions, an updated predictive model was derived. This new model resulted from a linear regression analysis of the data reported here combined with 31 previously studied 2 × 3 nucleotide internal loops. Most of the values for the parameters in this new predictive model are within experimental error of those of the previous models, suggesting that approximations and assumptions associated with the derivation of the previous nearest neighbor parameters were valid. The updated predictive model predicts free energies of 2 × 3 nucleotide internal loops within 0.4 kcal/mol, on average, of the experimental free energy values. Both the experimental values and the updated predictive model can be used to improve secondary structure prediction from sequence.

Nucleotide sequences of coat protein (CP) gene from 6 isolates of Apple stem pitting virus (ASPV) were obtained. Sequence analysis of CP gene showed nucleotide identities ranged from 68.7 to 99.7%. Phylogenetic analysis showed that all ASPV isolates fell into three groups. To a certain extent, the groups were related to the host origins of virus. The hypervariable and conserved region could be found in the CP gene. The variations of nucleotide sequence and predicted RNA secondary structure were analyzed for the CP gene, and the characteristic of RNA secondary structure of ASPV 5' and 3' untranslated region (UTR) were also studied. The result indicated that predicted RNA secondary structures could play an important role in studying the molecular variation of virus. The 3' UTR of ASPV RNA was predicted to contain four stem-loop structures, and it was likely to imply that the variation of RNA secondary structure was related to groups of phylogenetic analysis.

Bioinformatics has evolved from its initial function of storage and maintenance of biological data repositories to include the present-day computational techniques to analyze biological data. Secondary structure identification of the RNA sequence is important in bioinformatics. RNA needs to perform its biological functions and give a lead for predicting the tertiary structure of RNA. The secondary structure of the RNA sequence is used for the design and testing of pharmaceutical products. This chapter dealt with the classification of RNA molecules and followed by its secondary structure prediction techniques. The standard methods are base pairing and energetics based secondary structure prediction techniques. The base pair mode of prediction is based on a stacked pair, hairpin loop, bulge loop, interior loop, and multi-loop. Nussinov algorithm is one of the base pair formulation techniques that developed as a software. In the energy minimization method, Gibbs free energy is considered for RNA structure formation. Zuker’s algorithm is such an algorithm developed as software for RNA secondary structure prediction.

Stochastic context-free grammars (SCFGs) have been applied to predicting RNA secondary structure. The prediction of RNA secondary structure can be facilitated by incorporating with comparative sequence analysis. However, most of existing SCFG-based methods lack explicit phylogenic analysis of homologous RNA sequences, which is probably the reason why these methods are not ideal in practical application. Hence, we present a new SCFG-based method by integrating phylogenic analysis with the newly defined profile SCFG. The method can be summarized as: 1) we define a new profile SCFG, M, to depict consensus secondary structure of multiple RNA sequence alignment; 2) we introduce two distinct hidden Markov models, λ and λ′, to perform phylogenic analysis of homologous RNA sequences. Here, λ is for non-structural regions of the sequence and λ′, is for structural regions of the sequence; 3) we merge λ and λ′, into M to devise a combined model for prediction of RNA secondary structure. We tested our method on data sets constructed from the Rfam database. The sensitivity and specificity of our method are more accurate than those of the predictions by Pfold.

Improved Parameters for Prediction of RNA Secondary Structure and Insights into why RNA forms Double Helixes.- Anomalous Conformations of RNA Constituents: 2D NMR and Calculational Studies.- NMR Studies of Base-pair Kinetics of Nucleic Acids.- Picosecond Time Domain Spectroscopy of Structure and Dynamics in Nucleic Acids.- Z-RNA: A Left-handed Double Helix.- A Better Way to Make RNA for Physical Studies.- Mg2+-inner Sphere Complexes at "Ends" and "Bends" of Polynucleotides and their Potential Role as Long Range Inducers of Conformation Changes.- Pseudoknots in RNA: A Novel Folding Principle.- Proton NMR Studies of RNA's and Related Enzymes Using Isotope Labels.- GU Base Pairs and Variable Loop in Yeast tRNAAsp.- Correlation Between Crystal and Solution Structures in tRNA. Yeast tRNAPhe and tRNAAsp the Models for Free and Messenger RNA Bound tRNAs.- The Solution Structures of RNA Fragments Determined by Nuclear Overhauser Enhancement Measurements.- Molecular Dynamics Simulation of the Anticodon Arm of Phenylalanine Transfer RNA.- Anticodon-Anticodon Interactions and tRNA Sequence Comparison: Approaches to Codon Recognition.- Studies on N Labelled 5S RNA: Assignments in the Helix V Region of 5S RNA, and in the 5S/L25 Complex.- Equilibria in Ribosomal RNA Secondary Structure.- A Comparative Analysis of Structural Dynamics in 5S rRNA.- A Domain of 23S Ribosomal RNA in Search of a function.- The Three-Dimensional Organization of Escherichia coli Ribosomal RNA.- The Extremely Small Mitochondrial Ribosomal RNAs from Trypanosomes.- Mutagenesis of Ribosomal RNA as a Method to Investigate Interactions Between rRNA, mRNA and tRNA.- Ribosomal RNA at the Decoding Site of the tRNA-Ribosome Complex.- Double Stranded RNA in the Decoding of the mRNA by the Bacterial Ribosome.- Conformational Dynamics Involved in RNA Self-Splicing.- RNA Catalyzed Lariat Formation from Yeast Mitochondrial Pre-Ribosomal RNA.- Viroids: Structure Formation and function.- Author Index.

Prediction of RNA secondary structure by free energy minimization