RNA Structure Research Articles

A conserved class of viral RNA structures regulates translation reinitiation through dynamic ribosome interactions.

Certain viral RNAs encode proteins downstream of their main open reading frame, expressed through "termination-reinitiation" events. In some cases, structures located upstream of the first stop codon within these viral RNAs bind the ribosome, inhibiting ribosome recycling and inducing reinitiation. We used bioinformatics methods to identify new examples of viral reinitiation-stimulating RNAs and experimentally verified their secondary structure and function. We determined the structure of a representative viral RNA-ribosome complex using cryoelectron microscopy (cryo-EM). 3D classification and variability analyses reveal that the viral RNA structure can sample a range of conformations while remaining tethered to the ribosome, enabling the ribosome to find a reinitiation start site within a limited range of mRNA sequence. Evaluating the conserved features and constraints of this entire RNA class within the context of the cryo-EM reconstruction provides insight into mechanisms enabling reinitiation, a translation regulation strategy employed by many other viral and eukaryotic systems.

Segment-specific promoter activity for RNA synthesis in the genome of Oz virus, genus Thogotovirus.

Oz virus (OZV), a tick-borne, six-segmented negative-strand RNA virus in the genus Thogotovirus, caused a fatal human infection in Japan in 2023. To study viral RNA synthesis, we developed an OZV minigenome assay using mammalian cells. This revealed variations in promoter activities among the six genome segments. The "distal duplex," a double-stranded RNA structure beginning at the 11th nucleotide on the 5' end and the 10th on the 3' end, was found in all segments. A factor affecting promoter activity was the base pairing between the 12th nucleotide at the 5' end and the 11th at the 3' end, forming either G:C or A:U pairs. Disruption of this pairing caused a significant loss of promoter activity, emphasizing the importance of the distal duplex with at least six consecutive base pairs. Comparative analysis of genome terminal sequences suggests similar structural variations in the promoters of other species in Thogotovirus.

Polyamines enhance repeat-associated non-AUG translation from CCUG repeats by stabilizing the tertiary structure of RNA.

Repeat expansion disorders are caused by abnormal expansion of microsatellite repeats. Repeat-associated non-AUG (RAN) translation is one of the pathogenic mechanisms underlying repeat expansion disorders, but the exact molecular mechanism underlying RAN translation remains unclear. Polyamines are ubiquitous biogenic amines that are essential for cell proliferation and cellular functions. They are predominantly found in cells in complexes with RNA and influence many cellular events, but the relationship between polyamines and RAN translation is yet to be explored. Here, we show that, in both a cell-free protein synthesis system and cell culture, polyamines promote RAN translation of RNA containing CCUG repeats. The CCUG-dependent RAN translation is suppressed when cells are depleted of polyamines but can be recovered by the addition of polyamines. Thermal stability analysis revealed that the tertiary structure of the CCUG-repeat RNA is stabilized by the polyamines. Spermine was the most effective polyamine for stabilizing CCUG-repeat RNA and enhancing RAN translation. These results suggest that polyamines, particularly spermine, modulate RAN translation of CCUG-repeat RNA by stabilizing the tertiary structure of the repeat RNA.

Learning the language of life with AI.

In 2021, a year before ChatGPT took the world by storm amid the excitement about generative artificial intelligence (AI), AlphaFold 2 cracked the 50-year-old protein-folding problem, predicting three-dimensional (3D) structures for more than 200 million proteins from their amino acid sequences. This accomplishment was a precursor to an unprecedented burgeoning of large language models (LLMs) in the life sciences. That was just the beginning. In recent months, we have moved into a hyperaccelerated phase of new foundation models, pretrained on massive datasets, with the ability to perform a wide range of tasks that are helping us understand the structure, biology, evolution, and design of proteins, RNA, DNA, and ligands, as well as their biomolecular interactions. Unlike multimodal LLMs such as GPT-4, Gemini, and Claude, which process text, audio, and images, these large language of life models (LLLMs) are multiomic. That is to say, they are not only multimodal but pertain to different layers of molecular biology. For example, Evo, a foundation model trained on 2.7 million diverse phage and prokaryotic genomes (equivalent to about 300 billion DNA nucleotides), predicts the impact of variants in DNA, RNA, or proteins on structure and function, as well as how essential genes are to cell function, and can generate new DNA sequences.

Has AlphaFold3 achieved success for RNA?

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold, AlphaFold3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold3. We also compare its performance with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.

RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.

Despite the groundbreaking advances in deep learning-enabled methods for bimolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging due to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. We introduce RNAbpFlow, a novel sequence- and base-pair-conditioned SE(3)-equivariant flow matching model for generating RNA 3D structural ensemble. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking. RNAbpFlow is freely available at https://github.com/Bhattacharya-Lab/RNAbpFlow .

Increased unedited Alu RNA patterns found in cortex extracellular vesicles in Alzheimer's disease resemble hippocampus vasculature Alu RNA editing patterns but not cortex Alu RNA editing patterns.

Endogenous Alu RNAs form double-stranded RNAs recognized by double-stranded RNA sensors and activate IRF and NF-kB transcriptional paths and innate immunity. Deamination of adenosines to inosines by the ADAR family of enzymes, a process termed A-to-I editing, disrupts double-stranded RNA structure and prevents innate immune activation. Innate immune activation is observed in Alzheimer's disease, the most common form of dementia. We have previously reported loss of A-to-I editing in hippocampus vasculature, but no change in cortex or cortex vasculature, associated with Alzheimer's disease. Here, we investigated the status of Alu RNA A-to-I editing in cortex extracellular vesicles in Alzheimer's disease. We used existing RNA-seq data sets and the SPRINT software package to determine levels of Alu RNA A-to-I editing in cortex extracellular vesicles in Alzheimer's disease and control groups and compared these editing profiles to those found in both total cortex and hippocampus vasculature. We find substantial loss of Alu A-to-I editing in cortex extracellular vesicles in Alzheimer's disease. By measuring editing patterns on a gene-by-gene basis, we determined that editing patterns in cortex extracellular vesicles resemble editing patterns in hippocampus vasculature rather than total cortex. We conclude that hippocampus vasculature unedited Alu RNAs are packaged in extracellular vesicles, travel to the cortex, deliver their cargo and stimulate innate immunity and alter other basic biological processes contributing to Alzheimer's disease progression.

RNA G-quadruplex structure-based PROTACs for targeted DHX36 protein degradation and gene activity modulation in mammalian cells.

RNA G-quadruplexes (rG4s) are non-canonical secondary nucleic acid structures found in the transcriptome. They play crucial roles in gene regulation by interacting with G4-binding proteins (G4BPs) in cells. rG4-G4BP complexes have been associated with human diseases, making them important targets for drug development. Generating innovative tools to disrupt rG4-G4BP interactions will provide a unique opportunity to explore new biological mechanisms and potentially treat related diseases. Here, we have rationally designed anddeveloped a series of rG4-based proteolytic targeting chimeras (rG4-PROTACs) aimed at degrading G4BPs, such as DHX36, a specific G4BP that regulates gene expression by binding to and unraveling rG4 structures in messenger RNAs(mRNAs). Our comprehensive data and systematic analysis reveals that rG4-PROTACs predominantly and selectively degrade DHX36 through a proteosome-dependent mechanism, which promotes the formation of the rG4 structure in mRNA, leading to the translation inhibition of rG4-containing transcripts. Notably, rG4-PROTACs inhibit rG4-mediated APP protein expression, and impact the proliferative capacity of skeletal muscle stem cells by negatively regulating Gnai2 protein expression. In summary, rG4-PROTACs provide a new avenue to understand rG4-G4BP interactions and the biological implications of dysregulated G4BPs, promoting the development of PROTACs technology based on the non-canonical structure of nucleic acids.

Topologically constrained DNA-mediated one-pot CRISPR assay for rapid detection of viral RNA with single nucleotide resolution.

The widespread and evolution of RNA viruses, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the importance of fast identification of virus subtypes, particularly in non-laboratory settings. Rapid and inexpensive at-home testing of viral nucleic acids with single-base resolution remains a challenge. Topologically constrained DNA ring is engineered as substrates for the trans-cleavage of Cas13a to yield an accelerated post isothermal amplification. The capacity of CRISPR/Cas13a for discriminating single nucleotide variant (SNV) in viral genome is leveraged by designing synthetic mismatches and hairpin structure in CRISPR RNA (crRNA), enabling robust discrimination of different SARS-CoV-2 variants. Via optimisation of CasTDR3pot to be one-pot assay, CasTDR1pot can detect Omicron and its subvariants, with only a few copies in clinical samples in less than 30min without pre-amplification. The detection system boasts high sensitivity (0.1 aM), single-base specificity, and the advantage of a rapid "sample-to-answer" process, which takes only 30min. In the detection of SARS-CoV-2 clinical samples and their variant strains, CasTDR1pot has achieved 100% accuracy. Furthermore, the design of a portable signal-reading device facilitates user-friendly result interpretation. For the detection needs of different RNA viruses, the system can be adapted simply by designing the corresponding crRNA. Our study provides a rapid and accurate molecular diagnostic tool for point-of-care testing, epidemiological screening, and the detection of diseases associated with other RNA biomarkers with excellent single nucleotide differentiation, high sensitivity, and simplicity. National Key Research and Development Program of China (No. 2023YFB3208302), National Natural Science Foundation of China (No. 22377110, 22034004, 82402749, 82073787, 22122409), National Key Research and Development Program of China (No. 2021YFA1200104), Henan Province Fund for Cultivating Advantageous Disciplines (No. 222301420019).

NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation

RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures. NuFold is a deep neural network trained end-to-end for the output structure from the input sequence. NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. Benchmark study showed that NuFold clearly outperformed energy-based methods and demonstrated comparable results with existing state-of-the-art deep-learning-based methods. NuFold exhibited a particular advantage in building correct local geometries of RNA. Analyses of individual components in the NuFold pipeline indicated that the performance improved by utilizing metagenome sequences for multiple sequence alignment and increasing the number of recycling. NuFold is also capable of predicting multimer complex structures of RNA by linking the input sequences.

Scaffold-enabled high-resolution cryo-EM structure determination of RNA

Cryo-EM structure determination of protein-free RNAs has remained difficult with most attempts yielding low to moderate resolution and lacking nucleotide-level detail. These difficulties are compounded for small RNAs as cryo-EM is inherently more difficult for lower molecular weight macromolecules. Here we present a strategy for fusing small RNAs to a group II intron that yields high resolution structures of the appended RNA. We demonstrate this technology by determining the structures of the 86-nucleotide (nt) thiamine pyrophosphate (TPP) riboswitch aptamer domain and the recently described 210-nt raiA bacterial non-coding RNA involved in sporulation and biofilm formation. In the case of the TPP riboswitch aptamer domain, the scaffolding approach allowed visualization of the riboswitch ligand binding pocket at 2.5 Å resolution. We also determined the structure of the ligand-free apo state and observe that the aptamer domain of the riboswitch adopts an open Y-shaped conformation in the absence of ligand. Using this scaffold approach, we determined the structure of raiA at 2.5 Å in the core. Our versatile scaffolding strategy enables efficient RNA structure determination for a broad range of small to moderate-sized RNAs, which were previously intractable for high-resolution cryo-EM studies.

Revealing New Analytical Insights into RNA Complexes: Divalent siRNA Characterization by Liquid Chromatography and Mass Spectrometry.

Accurate characterization of therapeutic RNA, including purity and identity, is critical in drug discovery and development. Here, we utilize denaturing and non-denaturing chromatography for the analysis of ∼25 kDa divalent small interfering RNA (di-siRNA), which comprises a complex 2:1 triplex structure. Ion pair reversed-phase (IPRP) liquid chromatography (LC) experiments with UV absorbance and mass spectrometry (MS) showcase a single denaturing LC method for identity confirmation, impurity profiling, and sequencing with automated MS data interpretation. IPRP, size exclusion chromatography (SEC), and melting temperature (Tm) experiments showcase the need for consideration of chromatographic conditions in evaluating noncovalent siRNA structures─here, low-temperature IPRP experiments (generally considered "non-denaturing") indicate denaturation of the noncovalent complex for certain di-siRNA sequences, while SEC data indicate that di-siRNA aggregation can be vastly underestimated due to sample dilution prior to LC experiments. Furthermore, SEC data critically show the propensity of denatured di-siRNA samples to renature under SEC mobile phase conditions upon exposure to high ionic/salt solutions, corroborated by Tm experiments. This work highlights the need for consideration of the noncovalent nature of certain RNA therapeutics during sample preparation, method development, and analytical characterization and the often sequence-specific strength of complex formation, which may significantly affect analytical results obtained for such molecules. Attention to the highly dynamic nature of the duplex RNA structure, which is heavily influenced by its environment, must be considered during analytical method development.

Design of triplex-forming peptide nucleic acid-based fluorescent probes for forced intercalation sensing of double-stranded RNA structures.

The diverse functional roles of RNA within cells have led to a growing interest in developing RNA-binding fluorescent probes to investigate RNA functions. In particular, the probes for double-stranded RNA (dsRNA) structures are of significant value given the importance of the secondary and tertiary RNA structures on their biologic functions. This review highlights our recent efforts on the development of triplex-forming peptide nucleic acid (TFP)-based probes for fluorescence sensing of dsRNA structures. We demonstrated that the forced intercalation of asymmetric cyanine dyes integrated as base surrogate within the probes was useful for achieving significant light-up response toward target dsRNAs. We also showed that the TFP probes conjugated with small RNA-binding molecules facilitated the fluorescence sensing of biologic relevant dsRNAs containing unpaired nucleobases. The binding and fluorescence signaling functions of such probes were discussed, emphasizing their potential as analytical tools for studying dsRNA structures.

SARS-CoV-2 CoCoPUTs: analyzing GISAID and NCBI data to obtain codon statistics, mutations, and free energy over a multiyear period.

A consistent area of interest since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been the sequence composition of the virus and how it has changed over time. Many resources have been developed for the storage and analysis of SARS-CoV-2 data, such as GISAID (Global Initiative on Sharing All Influenza Data), NCBI, Nextstrain, and outbreak.info. However, relatively little has been done to compile codon usage data, codon-level mutation data, and secondary structure data into a single database. Here, we assemble the aforementioned data and many additional virus attributes in a new database entitled SARS-CoV-2 CoCoPUTs. We begin with an overview of the composition and overlap between two of the largest sources of SARS-CoV-2 sequence data: GISAID and NCBI Virus (GenBank). We then evaluate different types of sequence curation strategies to reduce the dataset of millions of sequences to only one sequence per Pango lineage variant. We then performed specific analyses on the coding sequences (CDSs), including calculating codon usage, codon pair usage, dinucleotides, junction dinucleotides, mutations, GC content, effective number of codons (ENCs), and effective number of codon pairs (ENCPs). We have also performed whole-genome secondary RNA structure prediction calculations for each variant, using the LinearPartition software and modified selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) data that are available online. Finally, we compiled all the data into our resource, SARS-CoV-2 CoCoPUTs, and paired many of the resulting statistics with variant proportion data over time in order to derive trends in viral evolution. Although the overall codon usage of SARS-CoV-2 did not change drastically, in line with the previous literature on this subject, we did observe that while overall GC% content decreased, GC% of the third position in the codon was more positive relative to overall GC% content between February 2021 and July 2023. Over the same interval, we noted that both synonymous and nonsynonymous mutations increased in number, with nonsynonymous mutations outpacing synonymous mutations at a rate of 3:1. We noted that the predicted whole-genome secondary structures nearly all contained the previously described virus-activated inhibitor of translation (VAIT) stem loops, validating for the first time their existence in a whole-genome secondary structure prediction for many SARS-CoV-2 variants (as opposed to previous local secondary structure predictions). We also separately produced a synonymous mutation-deprived set of SARS-CoV-2 variant sequences and repeated the secondary structure calculations on this set. This revealed an interesting trend of reduced ensemble free energy compared to the unaltered variant structures, indicating that synonymous mutations play a role in increasing the free energy of viral RNA molecules. These data both validate previous studies describing increases in viral free energy in human viruses over time and indicate a possible role for synonymous mutations in viral biology.

Chronic RNA G-quadruplex Accumulation in Aging and Alzheimer's Disease.

As the world population ages, new molecular targets in aging and Alzheimer's Disease (AD) are needed to combat the expected influx of new AD cases. Until now, the role of RNA structure in aging and neurodegeneration has largely remained unexplored. In this study, we examined human hippocampal postmortem tissue for the formation of RNA G-quadruplexes (rG4s) in aging and AD. We found that rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. We further found that neurons with accumulation of phospho-tau immunostaining contained rG4s, that rG4 structure can drive tau aggregation, and that rG4 staining density depended on APOE genotype in the human tissue examined. Combined with previous studies showing the dependence of rG4 structure on stress and the extreme power of rG4s at oligomerizing proteins, we propose a model of neurodegeneration in which chronic rG4 formation is linked to proteostasis collapse. These morphological findings suggest that further investigation of RNA structure in neurodegeneration is a critical avenue for future treatments and diagnoses.

DesiRNA: structure-based design of RNA sequences with a replica exchange Monte Carlo approach.

Designing RNA sequences that form a specific structure remains a challenge. Current computational methods often struggle with the complexity of RNA structures, especially when considering pseudoknots or restrictions related to RNA function. We developed DesiRNA, a computational tool for the design of RNA sequences based on the Replica Exchange Monte Carlo approach. It finds sequences that minimize a multiobjective scoring function, fulfill user-defined constraints and minimize the violation of restraints. DesiRNA handles pseudoknots, designs RNA-RNA complexes and sequences with alternative structures, prevents oligomerization of monomers, prevents folding into undesired structures and allows users to specify nucleotide composition preferences. In benchmarking tests, DesiRNA with a default simple scoring function solved all 100 puzzles in the Eterna100 benchmark within 24 h, outperforming all existing RNA design programs. With its ability to address complex RNA design challenges, DesiRNA holds promise for a range of applications in RNA research and therapeutic development.

The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure.

RNA viruses like SARS-CoV-2 have a high mutation rate, which contributes to their rapid evolution. The rate of mutations depends on the mutation type (e.g., A → C , A → G , etc.) and can vary between sites in the viral genome. Understanding this variation can shed light on the mutational processes at play, and is crucial for quantitative modeling of viral evolution. Using the millions of available SARS-CoV-2 full-genome sequences, we estimate rates of synonymous mutations for all 12 possible nucleotide mutation types and examine how much these rates vary between sites. We find a surprisingly high level of variability and several striking patterns: the rates of four mutation types suddenly increase at one of two gene boundaries; the rates of most mutation types strongly depend on a site's local sequence context, with up to 56-fold differences between contexts; consistent with a previous study, the rates of some mutation types are lower at sites engaged in RNA secondary structure. A simple log-linear model of these features explains ∼15-60% of the fold-variation of mutation rates between sites, depending on mutation type; more complex models only modestly improve predictive power out of sample. We estimate the fitness effect of each mutation based on the number of times it actually occurs versus the number of times it is expected to occur based on the model. We identify several small regions of the genome where synonymous or noncoding mutations occur much less often than expected, indicative of strong purifying selection on the RNA sequence that is independent of protein sequence. Overall, this work expands our basic understanding of SARS-CoV-2's evolution by characterizing the virus's mutation process at the level of individual sites and uncovering several striking mutational patterns that arise from unknown mechanisms.

RNA-TorsionBERT: leveraging language models for RNA 3D torsion angles prediction.

Predicting the 3D structure of RNA is an ongoing challenge that has yet to be completely addressed despite continuous advancements. RNA 3D structures rely on distances between residues and base interactions but also backbone torsional angles. Knowing the torsional angles for each residue could help reconstruct its global folding, which is what we tackle in this work. This paper presents a novel approach for directly predicting RNA torsional angles from raw sequence data. Our method draws inspiration from the successful application of language models in various domains and adapts them to RNA. We have developed a language-based model, RNA-TorsionBERT, incorporating better sequential interactions for predicting RNA torsional and pseudo-torsional angles from the sequence only. Through extensive benchmarking, we demonstrate that our method improves the prediction of torsional angles compared to state-of-the-art methods. In addition, by using our predictive model, we have inferred a torsion angle-dependent scoring function, called TB-MCQ, that replaces the true reference angles by our model prediction. We show that it accurately evaluates the quality of near-native predicted structures, in terms of RNA backbone torsion angle values. Our work demonstrates promising results, suggesting the potential utility of language models in advancing RNA 3D structure prediction. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/RNA-TorsionBERT. Supplementary data are available from the website.

Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.

A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference. Specifically, motivated by the clustered distribution of known RNA structures, a collection of distinct DL models is trained by iteratively leaving one cluster out. Each DL model hence serves as an expert on all but one cluster in the training data. Consequently, for an ID sequence, all but one DL model makes accurate predictions consistent with one another, while an OOD sequence yields highly inconsistent predictions among all DL models. Through consensus analysis of DL predictions, test sequences are categorized as ID or OOD. ID sequences are subsequently predicted by averaging the DL models in consensus, and OOD sequences are predicted using physics-based models. Instead of remediating generalization gaps with alternative approaches such as transfer learning and sequence alignment, MoEFold2D circumvents unpredictable ID-OOD gaps and combines the strengths of DL and physics-based models to achieve accurate ID and robust OOD predictions.

Linking the function of cis-acting RNA elements to coronavirus replication using interactomes.

RNA structures that are functionally important are defined as cis-acting RNA elements because their functions cannot be compensated for in trans. The cis-acting RNA elements in the 3' UTR of coronaviruses are important for replication; however, the mechanism linking the cis-acting RNA elements to their replication function remains to be established. In the present study, a comparison of the biological processes of the interactome and the replication efficiency between the 3' UTR cis-acting RNA elements in coronaviruses, including severe acute respiratory syndrome coronavirus 2, suggests that (i) the biological processes, including translation, protein folding and protein stabilization, derived from the analysis of the cis-acting RNA element interactome and (ii) the architecture of the cis-acting RNA elements and their interactomes are highly correlated with coronavirus replication. In addition, alteration of the interactome using the compound 5-benzyloxygramine can cause reduced coronavirus replication, reinforcing the connection between cis-acting RNA elements and replication by interactome. Together, these results link cis-acting RNA elements to the coronavirus replication and establish a model to analyse the cis-acting RNA elements in the replication of RNA viruses by interactome.