RNA Secondary Structure Research Articles

Structural Unfolding of G-Quadruplexes: From Small Molecules to Antisense Strategies.

G-quadruplexes (G4s) are non-canonical nucleic acid secondary structures that have gathered significant interest in medicinal chemistry over the past two decades due to their unique structural features and potential roles in a variety of biological processes and disorders. Traditionally, research efforts have focused on stabilizing G4s, while in recent years, the attention has progressively shifted to G4 destabilization, unveiling new therapeutic perspectives. This review provides an in-depth overview of recent advances in the development of small molecules, starting with the controversial role of TMPyP4. Moreover, we described effective metal complexes in addition to G4-disrupting small molecules as well as good G4 stabilizing ligands that can destabilize G4s in response to external stimuli. Finally, we presented antisense strategies as a promising approach for destabilizing G4s, with a particular focus on 2'-OMe antisense oligonucleotide, peptide nucleic acid, and locked nucleic acid. Overall, this review emphasizes the importance of understanding G4 dynamics as well as ongoing efforts to develop selective G4-unfolding strategies that can modulate their biological function and therapeutic potential.

Evaluation of the effect of RNA secondary structure on Cas13d-mediated target RNA cleavage

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas13d system was adapted as a powerful tool for targeting viral RNA sequences, making it a promising approach for antiviral strategies. Understanding the influence of template RNA structure on Cas13d binding and cleavage efficiency is crucial for optimizing its therapeutic potential. In this study, we investigated the effect of local RNA secondary structure on Cas13d activity. To do so, we varied the stability of a hairpin structure containing the SARS-CoV-2 target sequence, allowing us to determine the threshold RNA stability at which Cas13d activity is affected. Our results demonstrate that Cas13d possesses the ability to effectively bind and cleave highly stable RNA structures. Notably, we only observed a decrease in Cas13d activity in the case of exceptionally stable RNA hairpins with completely base-paired stems, which are rarely encountered in natural RNA molecules. Comparison of Cas13d and RNA interference (RNAi)-mediated cleavage of the same RNA targets demonstrated that RNAi is more sensitive for local target RNA structure than Cas13d. These results underscore the suitability of the CRISPR-Cas13d system for targeting viruses with highly structured RNA genomes.

Biomolecular condensates can enhance pathological RNA clustering.

Intracellular aggregation of repeat expanded RNA has been implicated in many neurological disorders. Here, we study the role of biomolecular condensates on irreversible RNA clustering. We find that physiologically relevant and disease-associated repeat RNAs spontaneously undergo an age-dependent percolation transition inside multi-component protein-nucleic acid condensates to form nanoscale clusters. Homotypic RNA clusters drive the emergence of multiphasic condensate structures with an RNA-rich solid core surrounded by an RNA-depleted fluid shell. The timescale of the RNA clustering, which drives a liquid-to-solid transition of biomolecular condensates, is determined by the sequence features, stability of RNA secondary structure, and repeat length. Importantly, G3BP1, the core scaffold of stress granules, introduces heterotypic buffering to homotypic RNA-RNA interactions and impedes intra-condensate RNA clustering in an ATP-independent manner. Our work suggests that biomolecular condensates can act as sites for RNA aggregation. It also highlights the functional role of RNA-binding proteins in suppressing aberrant RNA phase transitions.

Regulatory Networks Underlying Plant Responses and Adaptation to Cold Stress.

Cold is a key determinant for plant growth and flowering time as well as an important environmental factor limiting plant growth and development. Recent studies have revealed the complex regulatory networks associated with plant responses to cold and identified their interconnections with signaling pathways related to light, the circadian clock, plant hormones, and pathogen defense. In this article, we review recent advances in understanding the molecular basis of cold perception and signal transduction pathways. We also summarize recent developments in the study of cold-responsive growth and flowering. Finally, we propose future directions for the study of long-term cold sensing, RNA secondary structures in response to cold, and the development of cold-tolerant and high-yield crops.

Conformational Dynamics of Influenza A Virus Ribonucleoprotein Complexes during RNA Synthesis.

Viral ribonucleoproteins (vRNPs) are the cornerstones of viral proliferation, as they form the macromolecular complexes that are responsible for the transcription and replication of most single-stranded RNA viruses. The influenza A virus (IAV) polymerase catalyzes RNA synthesis within the context of vRNPs where genomic viral RNA (vRNA) is packaged by the viral nucleoprotein (NP). We used high-speed atomic force microscopy and electron microscopy to study the conformational dynamics of individual IAV recombinant RNPs (rRNPs) during RNA synthesis. The rRNPs present an annular organization that allows for the real-time tracking of conformational changes in the NP-vRNA template caused by the advancing polymerase. We demonstrate that the rRNPs undergo a well-defined conformational cycle during RNA synthesis, which can be interpreted in light of previous transcription models. We also present initial estimations of the average RNA synthesis rate in the rRNP and its dependence on the nucleotide concentration and stability of the nascent RNA secondary structures. Furthermore, we provide evidence that rRNPs can perform consecutive cycles of RNA synthesis, accounting for their ability to recycle and generate multiple copies of RNA.

Ecological and evolutionary dynamics of CRISPR-Cas systems in Clostridium botulinum: Insights from genome mining and comparative analysis

Understanding the prevalence and distribution of CRISPR-Cas systems across different strains can illuminate the ecological and evolutionary dynamics of Clostridium botulinum populations. In this study, we conducted genome mining to characterize the CRISPR-Cas systems of C. botulinum strains. Our analysis involved retrieving complete genome sequences of these strains and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Subsequently, we performed an analysis of homology in spacer sequences from identified CRISPR arrays to investigate and characterize the range of targeted phages and plasmids. Additionally, we investigated the evolutionary trajectory of C. botulinum strains under selective pressures from foreign invasive DNA. Our findings revealed that 306 strains possessed complete CRISPR-Cas structures, comprising 58% of the studied C. botulinum strains. Secondary structure prediction of consensus repeats indicated that subtype II-C, with longer stems compared to subtypes ID and IB, tended to form more stable RNA secondary structures. Moreover, protospacer motif analysis demonstrated that strains with subtype IB CRISPR-Cas systems exhibited 5′-CGG-3′, 5′-CC-3′, and 5′-CAT-3′ motifs in the 3′ flanking regions of protospacers. The diversity observed in CRISPR-Cas systems indicated their classification into subtypes IB, ID, II-C, III-B, and III-D. Furthermore, our results showed that systems with subtype ID and III-D frequently harbored similar spacer patterns. Moreover, analysis of spacer sequences homology with phage and prophage genomes highlighted the specific activities exhibited by subtype IB and III-B against phages and plasmids, providing valuable insights into the functional specialization within these systems.

Comprehensive survey of conserved RNA secondary structures in full-genome alignment of Hepatitis C virus

Hepatitis C virus (HCV) is a plus-stranded RNA virus that often chronically infects liver hepatocytes and causes liver cirrhosis and cancer. These viruses replicate their genomes employing error-prone replicases. Thereby, they routinely generate a large ‘cloud’ of RNA genomes (quasispecies) which—by trial and error—comprehensively explore the sequence space available for functional RNA genomes that maintain the ability for efficient replication and immune escape. In this context, it is important to identify which RNA secondary structures in the sequence space of the HCV genome are conserved, likely due to functional requirements. Here, we provide the first genome-wide multiple sequence alignment (MSA) with the prediction of RNA secondary structures throughout all representative full-length HCV genomes. We selected 57 representative genomes by clustering all complete HCV genomes from the BV-BRC database based on k-mer distributions and dimension reduction and adding RefSeq sequences. We include annotations of previously recognized features for easy comparison to other studies. Our results indicate that mainly the core coding region, the C-terminal NS5A region, and the NS5B region contain secondary structure elements that are conserved beyond coding sequence requirements, indicating functionality on the RNA level. In contrast, the genome regions in between contain less highly conserved structures. The results provide a complete description of all conserved RNA secondary structures and make clear that functionally important RNA secondary structures are present in certain HCV genome regions but are largely absent from other regions. Full-genome alignments of all branches of Hepacivirus C are provided in the supplement.

Intronic RNA secondary structural information captured for the human MYC pre-mRNA

Abstract To address the lack of intronic reads in secondary structure probing data for the human MYC pre-mRNA, we developed a method that combines spliceosomal inhibition with RNA probing and sequencing. Here, the SIRP-seq method was applied to study the secondary structure of human MYC RNAs by chemically probing HeLa cells with dimethyl sulfate in the presence of the small molecule spliceosome inhibitor pladienolide B. Pladienolide B binds to the SF3B complex of the spliceosome to inhibit intron removal during splicing, resulting in retained intronic sequences. This method was used to increase the read coverage over intronic regions of MYC. The purpose for increasing coverage across introns was to generate complete reactivity profiles for intronic sequences via the DMS-MaPseq approach. Notably, depth was sufficient for analysis by the program DRACO, which was able to deduce distinct reactivity profiles and predict multiple secondary structural conformations as well as their suggested stoichiometric abundances. The results presented here provide a new method for intronic RNA secondary structural analyses, as well as specific structural insights relevant to MYC RNA splicing regulation and therapeutic targeting.

Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites.

RNA secondary structures play essential roles in the formation of the tertiary structure and function of a transcript. Recent genome-wide studies highlight significant potential for RNA structures in the mammalian genome. However, a major challenge is assigning functional roles to these structured RNAs. In this study, we conduct a guilt-by-association analysis of clusters of computationally predicted conserved RNA structure (CRSs) in human untranslated regions (UTRs) to associate them with gene functions. We filtered a broad pool of ∼500 000 human CRSs for UTR overlap, resulting in 4734 and 24 754 CRSs from the 5' and 3' UTR of protein-coding genes, respectively. We separately clustered these CRSs for both sets using RNAscClust, obtaining 793 and 2403 clusters, each containing an average of five CRSs per cluster. We identified overrepresented binding sites for 60 and 43 RNA-binding proteins co-localizing with the clustered CRSs. Furthermore, 104 and 441 clusters from the 5' and 3' UTRs, respectively, showed enrichment for various Gene Ontologies, including biological processes such as 'signal transduction', 'nervous system development', molecular functions like 'transferase activity'and the cellular components such as 'synapse' among others. Our study shows that significant functional insights can be gained by clustering RNA structures based on their structural characteristics.

Investigating the role of killer cell immunoglobulin-like receptors and human leukocyte antigen genetic variants in hepatitis C virus infection.

The genetic diversity of killer cell immunoglobulin-like receptors (KIRs) and human leukocyte antigen (HLA) genes influences the host's immune response to viral pathogens. This study aims to explore the impact of five single nucleotide polymorphisms (SNPs) in KIR3DL2 and HLA-A genes on hepatitis C virus (HCV) infection. A total of 2251 individuals were included in the case-control study. SNPs including KIR3DL2 rs11672983, rs3745902, rs1654644, and HLA-A rs3869062, rs12202296 were genotyped. By controlling various confounding factors using a modified logistic regression model, as well as incorporating stratified analysis, joint effects analysis, and multidimensional bioinformatics analysis, we analyzed the relationship between SNPs and HCV infection. The logistic regression analysis showed a correlation between KIR3DL2 rs11672983 AA, KIR3DL2 rs3745902 TT, and increased HCV susceptibility (p < 0.01). Stratified analysis indicated that KIR3DL2 rs1654644 and HLA-A rs3869062 also heightened HCV susceptibility in certain subgroups. A linear trend of rising HCV infection rates was observed when combining KIR3DL2 rs11672983 AA and KIR3DL2 rs3745902 TT (ptrend = 0.007). Bioinformatics analysis suggested these SNPs' regulatory potential and their role in altering messenger RNA secondary structure, implying their functional relevance in HCV susceptibility. Our findings indicate that KIR3DL2 rs11672983 AA and KIR3DL2 rs3745902 TT are significantly associated with increased susceptibility to HCV infection.

Alternative splicing of lncRNA LAIR fine-tunes the regulation of neighboring yield-related gene LRK1 expression in rice.

The diversity in alternative splicing of long noncoding RNAs (lncRNAs) poses a challenge for functional annotation of lncRNAs. Moreover, little is known on the effects of alternatively spliced lncRNAs on crop yield. In this study, we cloned nine isoforms resulting from the alternative splicing of the lncRNA LAIR in rice. The LAIR isoforms are generated via alternative 5'/3' splice sites and different combinations of specific introns. All LAIR isoforms activate the expression of the neighboring LRK1 gene and enhance yield-related rice traits. In addition, there are slight differences in the binding ability of LAIR isoforms to the epigenetic modification-related proteins OsMOF and OsWDR5, which affect the enrichment of H4K16ac and H3K4me3 at the LRK1 locus, and consequently fine-tune the regulation of LRK1 expression and yield-related traits. These differences in binding may be caused by polymorphic changes to the RNA secondary structure resulting from alternative splicing. It was also observed that the composition of LAIR isoforms was sensitive to abiotic stress. These findings suggest that the alternative splicing of LAIR leads to the formation of a functional transcript population that precisely regulates yield-related gene expression, which may be relevant for phenotypic polymorphism-based crop breeding under changing environmental conditions.

Median and small parsimony problems on RNA trees.

Noncoding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis. We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labeled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility. https://github.com/bmarchand/rna\_small\_parsimony.

An intricate balancing act: Upstream and downstream frameshift co-regulatory elements.

Targeting ribosomal frameshifting has emerged as a potential therapeutic intervention strategy against Covid-19. During ribosomal translation, a fraction of elongating ribosomes slips by one base in the 5' direction and enters a new reading frame for viral protein synthesis. Any interference with this process profoundly affects viral replication and propagation. For Covid-19, two RNA sites associated with ribosomal frameshifting for SARS-CoV-2 are positioned on the 5' and 3' of the frameshifting residues. Although much attention has been on the 3' frameshift element (FSE), the 5' stem-loop (attenuator hairpin, AH) can play a role. The formation of AH has been suggested to occur as refolding of the 3' RNA structure is triggered by ribosomal unwinding. However, the attenuation activity and the relationship between the two regions are unknown. To gain more insight into these two related viral RNAs and to further enrich our understanding of ribosomal frameshifting for SARS-CoV-2, we explore the RNA folding of both 5' and 3' regions associated with frameshifting. Using our graph-theory-based modeling tools to represent RNA secondary structures, "RAG" (RNA- As-Graphs), and conformational landscapes to analyze length-dependent conformational distributions, we show that AH coexists with the 3-stem pseudoknot of the 3' FSE (graph 3_6 in our dual graph notation) and alternative pseudoknot (graph 3_3) but less likely with other 3' FSE alternative folds (such as 3-way junction 3_5). This is because an alternative length-dependent Stem 1 (AS1) can disrupt the FSE pseudoknots and trigger other folds. In addition, we design four mutants for long lengths that stabilize or disrupt AH, AS1 or FSE pseudoknot to illustrate the deduced AH/AS1 roles and favor the 3_5, 3_6 or stem-loop. These mutants further show how a strengthened pseudoknot can result from a weakened AS1, while a dominant stem-loop occurs with a strengthened AS1. These structural and mutational insights into both ends of the FSE in SARS-CoV-2 advance our understanding of the SARS-CoV-2 frameshifting mechanism by suggesting a sequence of length-dependent folds, which in turn define potential therapeutic intervention techniques involving both elements. Our work also highlights the complexity of viral landscapes with length-dependent folds, and challenges in analyzing these multiple conformations.

Parallel degradome-seq and DMS-MaPseq substantially revise the miRNA biogenesis atlas in Arabidopsis.

MicroRNAs (miRNAs) are produced from highly structured primary transcripts (pri-miRNAs) and regulate numerous biological processes in eukaryotes. Due to the extreme heterogeneity of these structures, the initial processing sites of plant pri-miRNAs and the structural rules that determine their processing have been predicted for many miRNAs but remain elusive for others. Here we used semi-active DCL1 mutants and advanced degradome-sequencing strategies to accurately identify the initial processing sites for 147 of 326 previously annotated Arabidopsis miRNAs and to illustrate their associated pri-miRNA cleavage patterns. Elucidating the in vivo RNA secondary structures of 73 pri-miRNAs revealed that about 95% of them differ from in silico predictions, and that the revised structures offer clearer interpretation of the processing sites and patterns. Finally, DCL1 partners Serrate and HYL1 could synergistically and independently impact processing patterns and in vivo RNA secondary structures of pri-miRNAs. Together, our work sheds light on the precise processing mechanisms of plant pri-miRNAs.

Evaluation of RNA Secondary Stem-Loop Structures in the UTRs of Mouse Hepatitis Virus as New Therapeutic Targets.

MHV-A59 is a beta-coronavirus that causes demyelinating encephalitis and hepatitis in mice. Recently, the mouse infection model of MHV-A59 has been used as an alternative animal infection model for SARS-CoV and SARS-CoV-2, aiding the development of new antiviral drugs. In this study, the MHV-A59 model was employed to investigate the potential of SARS-CoV-2 UTRs as new targets for antiviral drugs. Optimal targets within the MHV-A59 UTRs were identified using a shRNA and siRNA design tool, focusing on RNA secondary stem-loop (SL) structures in the UTRs. We then examined whether the designed RNAi constructs could inhibit MHV-A59 replication. In the 5'UTR, the stem-loop 1 (SL1) was identified as the most effective target, while in the 3'UTR, the minimal element for the initiation of negative-strand RNA synthesis (MIN) proved to be the most effective. Importantly, siRNAs targeting SL1 and MIN structures significantly reduced total RNA synthesis, negative-strand genomic RNA synthesis, subgenomic (sg) RNA synthesis, viral titer, and the plaque size of MHV-A59 compared to the control. Although not statistically significant, the combination of siSL1 and siMIN had a stronger effect on inhibiting MHV-A59 replication than either siRNA monotherapy. Interestingly, while the SL1 structure is present in both MHV and SARS-CoV-2, the MIN structure is unique to MHV. Thus, the SL1 of SARS-CoV-2 may represent a novel and promising target for RNAi-based antiviral drugs.

Identification and Genomic Characterization of Two Novel Hepatoviruses in Shrews from Yunnan Province, China.

Hepatitis A virus (HAV), a member of the genus Hepatovirus (Picornaviridae HepV), remains a significant viral pathogen, frequently causing enterically transmitted hepatitis worldwide. In this study, we conducted an epidemiological survey of HepVs carried by small terrestrial mammals in the wild in Yunnan Province, China. Utilizing HepV-specific broad-spectrum RT-PCR, next-generation sequencing (NGS), and QNome nanopore sequencing (QNS) techniques, we identified and characterized two novel HepVs provisionally named EpMa-HAV and EpLe-HAV, discovered in the long-tailed mountain shrew (Episoriculus macrurus) and long-tailed brown-toothed shrew (Episoriculus leucops), respectively. Our sequence and phylogenetic analyses of EpMa-HAV and EpLe-HAV indicated that they belong to the species Hepatovirus I (HepV-I) clade II, also known as the Chinese shrew HepV clade. Notably, the codon usage bias pattern of novel shrew HepVs is consistent with that of previously identified Chinese shrew HepV. Furthermore, our structural analysis demonstrated that shrew HepVs differ from other mammalian HepVs in RNA secondary structure and exhibit variances in key protein sites. Overall, the discovery of two novel HepVs in shrews expands the host range of HepV and underscores the existence of genetically diverse animal homologs of human HAV within the genus HepV.

Therapeutic Use of G4-Ligands in Cancer: State-of-the-Art and Future Perspectives.

G-quadruplexes (G4s) are guanine-rich non-canonical secondary structures of nucleic acids that were identified in vitro almost half a century ago. Starting from the early 1980s, these structures were also observed in eukaryotic cells, first at the telomeric level and later in regulatory regions of cancer-related genes, in regulatory RNAs and within specific cell compartments such as lysosomes, mitochondria, and ribosomes. Because of the involvement of these structures in a large number of biological processes and in the pathogenesis of several diseases, including cancer, the interest in G4 targeting has exponentially increased in the last few years, and a great number of novel G4 ligands have been developed. Notably, G4 ligands represent a large family of heterogeneous molecules that can exert their functions by recognizing, binding, and stabilizing G4 structures in multiple ways. Regarding anti-cancer activity, the efficacy of G4 ligands was originally attributed to the capability of these molecules to inhibit the activity of telomerase, an enzyme that elongates telomeres and promotes endless replication in cancer cells. Thereafter, novel mechanisms through which G4 ligands exert their antitumoral activities have been defined, including the induction of DNA damage, control of gene expression, and regulation of metabolic pathways, among others. Here, we provided a perspective on the structure and function of G4 ligands with particular emphasis on their potential role as antitumoral agents. In particular, we critically examined the problems associated with the clinical translation of these molecules, trying to highlight the main aspects that should be taken into account during the phases of drug design and development. Indeed, taking advantage of the successes and failures, and the more recent technological progresses in the field, it would be possible to hypothesize the development of these molecules in the future that would represent a valid option for those cancers still missing effective therapies.

Biomolecular Condensates Can Enhance Pathological RNA Clustering.

Intracellular aggregation of repeat expanded RNA has been implicated in many neurological disorders. Here, we study the role of biomolecular condensates on irreversible RNA clustering. We find that physiologically relevant and disease-associated repeat RNAs spontaneously undergo an age-dependent percolation transition inside multi-component protein-nucleic acid condensates to form nanoscale clusters. Homotypic RNA clusters drive the emergence of multiphasic condensate structures with an RNA-rich solid core surrounded by an RNA-depleted fluid shell. The timescale of the RNA clustering, which drives a liquid-to-solid transition of biomolecular condensates, is determined by the sequence features, stability of RNA secondary structure, and repeat length. Importantly, G3BP1, the core scaffold of stress granules, introduces heterotypic buffering to homotypic RNA-RNA interactions and impedes intra-condensate RNA clustering in an ATP-independent manner. Our work suggests that biomolecular condensates can act as sites for RNA aggregation. It also highlights the functional role of RNA-binding proteins in suppressing aberrant RNA phase transitions.

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms.

Application analysis of heuristic algorithms integrating dynamic programming in RNA secondary structure prediction

Ribonucleic acid is a crucial biomolecule in living organisms, with various types. To promote the research process of ribonucleic acid function, this study is for analyzing the utilization of heuristic algorithms on the ground of fusion dynamic programming in predicting the secondary structure of ribonucleic acid. Research on novel use of tree models for RNA secondary structure comparison, and use heuristic algorithms to optimize the multi branch structure comparison of tree models. Firstly, this study utilized dynamic programming algorithms to construct a comparison matrix and successfully found the backtracking path in the matrix. Meanwhile, for ensuring that the structural information of ribonucleic acid is not lost during the comparative analysis process, the study applies the idea of heuristic algorithms to calculate the optimal comparison between multi branched loops. Finally, the weights are adjusted using neural network algorithms to predict the optimal alignment structure. The results showed that the fusion dynamic programming heuristic algorithm achieved generalization performance of 0.928, 0.856, 0.842, and 0.793 on the target base data test sets of humans, mice, yeast, and spotted fish, respectively. Compared with the SimTree algorithm, the generalization performance has been improved by 15.13 %, 27.38 %, 27.77 %, and 38.88 %, respectively. In summary, the application of heuristic algorithms integrating dynamic programming in predicting the secondary structure of ribonucleic acid has good predictive performance. This has reference value for a deeper understanding of the structure and function relationship of ribonucleic acid.