Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

Unknotting RNA: A method to resolve computational artifacts

RNA 3D structure prediction often encounters entanglements, computational artifacts that complicate structural models, resulting in their exclusion from further studies despite the potentially accurate prediction of regions outside the entanglement. This study presents a protocol aimed at resolving such issues in RNA models while preserving the overall 3D fold and structural integrity. By employing the SPQR coarse-grained model and short Molecular Dynamics simulations, the protocol imposes energy terms that enable selective modifications to disentangle structures without causing significant distortions. The method was validated on 195 entangled RNA models from CASP15 and RNA-Puzzles, successfully resolving over 70% of interlaces and approximately 40% of lassos, with minimal impact on the original geometry but notable improvement in ClashScore. The efficiency of untangling conformations that are unequivocally classified as artifacts is 81%. Certain cases, particularly those involving dense packing of atoms or complex secondary structures, posed challenges that limited the efficiency of the method. In this paper, we present quantitative results from the application of the protocol and discuss examples of both successfully disentangled and unresolved structures. We show a viable approach for refining models previously deemed unsuitable due to topological artifacts.

Toward Increasing the Credibility of RNA Design

In the problem of RNA design, also known as inverse folding, RNA sequences are predicted that achieve the desired secondary structure at the lowest possible free energy and under certain constraints. The designed sequences have applications in synthetic biology and RNA-based nanotechnologies. There are also known cases of the successful use of inverse folding to discover previously unknown noncoding RNAs. Several computational methods have been dedicated to the problem of RNA design. They differ by algorithm and additional parameters, e.g., those determining the goal function in the sequence optimization process. Users can obtain many promising RNA sequences quite easily. The more difficult issue is to critically evaluate them and select the most favorable and reliable sequence that form1s the expected RNA structure. The latter problem is addressed in this paper. We propose an RNA design protocol extended to include sequence evaluation, for which a 3D structure is used. Experiments show that the accuracy of RNA design can be improved by adding a 3D structure prediction and analysis step.

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well, but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. Our aim is to identify factors influencing performance variation, such as RNA family diversity, sequence length, RNA type, multiple sequence alignment (MSA) quality, and deep learning model architecture. We show that generally ML-based methods perform much better than non-ML methods on most RNA targets, although the performance difference isn't substantial when working with unseen novel or synthetic RNAs. The quality of the MSA and secondary structure prediction both play an important role and most methods aren't able to predict non-Watson-Crick pairs in the RNAs. Overall among the automated 3D RNA structure prediction methods, DeepFoldRNA has the best prediction results followed by DRFold as the second best method. Finally, we also suggest possible mitigations to improve the quality of the prediction for future method development.

Untangling the pseudoknots of SARS-CoV-2: Insights into structural heterogeneity and plasticity

Since the onset of the COVID-19 pandemic, one productive area of research has focused on the intricate two- and three-dimensional structures taken on by SARS-CoV-2's RNA genome. These structures control essential viral processes, making them tempting targets for therapeutic intervention. This review focuses on two such structured regions, the frameshift stimulation element (FSE), which controls the translation of viral protein, and the 3' untranslated region (3' UTR), which is thought to regulate genome replication. For the FSE, we discuss its canonical pseudoknot's threaded and unthreaded topologies, as well as the diversity of competing two-dimensional structures formed by local and long-distance base pairing. For the 3' UTR, we review the evidence both for and against the formation of its replication-enabling pseudoknot.

RNAtango: Analysing and comparing RNA 3D structures via torsional angles

RNA molecules, essential for viruses and living organisms, derive their pivotal functions from intricate 3D structures. To understand these structures, one can analyze torsion and pseudo-torsion angles, which describe rotations around bonds, whether real or virtual, thus capturing the RNA conformational flexibility. Such an analysis has been made possible by RNAtango, a web server introduced in this paper, that provides a trigonometric perspective on RNA 3D structures, giving insights into the variability of examined models and their alignment with reference targets. RNAtango offers comprehensive tools for calculating torsion and pseudo-torsion angles, generating angle statistics, comparing RNA structures based on backbone torsions, and assessing local and global structural similarities using trigonometric functions and angle measures. The system operates in three scenarios: single model analysis, model-versus-target comparison, and model-versus-model comparison, with results output in text and graphical formats. Compatible with all modern web browsers, RNAtango is accessible freely along with the source code. It supports researchers in accurately assessing structural similarities, which contributes to the precision and efficiency of RNA modeling.

Rational Design Problematics of Peptide Nucleic Acids as SARS-CoV-2 Inhibitors

The use of viral protein inhibitors has shown to be insufficiently effective in the case of highly variable SARS-CoV-2. In this work, we examined the possibility of designing agents that bind to a highly conserved region of coronavirus (+)RNA. We demonstrated that while the design of antisense RNAs is based on the complementary interaction of nitrogenous bases, it is possible to use semirigid docking methods in the case of unnatural peptide nucleic acids. The transition from N-(2-aminoethyl)glycine chain to a more conformationally rigid piperidine-containing backbone allowed us to significantly increase the affinity of structures to the target RNA.

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

Conserved structures and dynamics in 5'-proximal regions of Betacoronavirus RNA genomes

Betacoronaviruses are a genus within the Coronaviridae family of RNA viruses. They are capable of infecting vertebrates and causing epidemics as well as global pandemics in humans. Mitigating the threat posed by Betacoronaviruses requires an understanding of their molecular diversity. The development of novel antivirals hinges on understanding the key regulatory elements within the viral RNA genomes, in particular the 5'-proximal region, which is pivotal for viral protein synthesis. Using a combination of cryo-electron microscopy, atomic force microscopy, chemical probing, and computational modeling, we determined the structures of 5'-proximal regions in RNA genomes of Betacoronaviruses from four subgenera: OC43-CoV, SARS-CoV-2, MERS-CoV, and Rousettus bat-CoV. We obtained cryo-electron microscopy maps and determined atomic-resolution models for the stem-loop-5 (SL5) region at the translation start site and found that despite low sequence similarity and variable length of the helical elements it exhibits a remarkable structural conservation. Atomic force microscopy imaging revealed a common domain organization and a dynamic arrangement of structural elements connected with flexible linkers across all four Betacoronavirus subgenera. Together, these results reveal common features of a critical regulatory region shared between different Betacoronavirus RNA genomes, which may allow targeting of these RNAs by broad-spectrum antiviral therapeutics.

Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis

SARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multiple sequence alignment with related coronaviruses as a starting point. We applied a computational pipeline developed for identifying non-coding RNA elements. Our pipeline employed three different RNA structural prediction approaches. We identified forty genomic regions likely to harbor structures, with ten of them showing three-way consensus substructure predictions among our predictive utilities. We conducted intracomparisons of the predictive utilities within the pipeline and intercomparisons with four previously published SARS-CoV-2 structural datasets. While there was limited agreement on the precise structure, different approaches seemed to converge on regions likely to contain structures in the viral genome. By comparing and combining various computational approaches, we can predict regions most likely to form structures, as well as a probable structure or ensemble of structures. These predictions can be used to guide surveillance, prophylactic measures, or therapeutic efforts. Data and scripts employed in this study may be found at https://doi.org/10.5281/zenodo.8298680.

RNA tertiary structure prediction using RNAComposer in CASP15

As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints

Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.

LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules

Many RNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Surprisingly, LinearCoFold and LinearCoPartition's predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold's prediction correlates better with the wet lab results than RNAcofold's.

When will RNA get its AlphaFold moment?

The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.

CovidShiny: An Integrated Web Tool for SARS-CoV-2 Mutation Profiling and Molecular Diagnosis Assay Evaluation In Silico

The coronavirus disease 2019 (COVID-19) pandemic is still ongoing, with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continuing to evolve and accumulate mutations. While various bioinformatics tools have been developed for SARS-CoV-2, a well-curated mutation-tracking database integrated with in silico evaluation for molecular diagnostic assays is currently unavailable. To address this, we introduce CovidShiny, a web tool that integrates mutation profiling, in silico evaluation, and data download capabilities for genomic sequence-based SARS-CoV-2 assays and data download. It offers a feasible framework for surveilling the mutation of SARS-CoV-2 and evaluating the coverage of the molecular diagnostic assay for SARS-CoV-2. With CovidShiny, we examined the dynamic mutation pattern of SARS-CoV-2 and evaluated the coverage of commonly used assays on a large scale. Based on our in silico analysis, we stress the importance of using multiple target molecular diagnostic assays for SARS-CoV-2 to avoid potential false-negative results caused by viral mutations. Overall, CovidShiny is a valuable tool for SARS-CoV-2 mutation surveillance and in silico assay design and evaluation.

The Highly Conserved Stem-Loop II Motif Is Dispensable for SARS-CoV-2

The stem-loop II motif (s2m) is an RNA structural element that is found in the 3' untranslated region (UTR) of many RNA viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Though the motif was discovered over 25 years ago, its functional significance is unknown. In order to understand the importance of s2m, we created viruses with deletions or mutations of the s2m by reverse genetics and also evaluated a clinical isolate harboring a unique s2m deletion. Deletion or mutation of the s2m had no effect on growth in vitro or on growth and viral fitness in Syrian hamsters in vivo. We also compared the secondary structure of the 3' UTR of wild-type and s2m deletion viruses using selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) and dimethyl sulfate mutational profiling and sequencing (DMS-MaPseq). These experiments demonstrate that the s2m forms an independent structure and that its deletion does not alter the overall remaining 3'-UTR RNA structure. Together, these findings suggest that s2m is dispensable for SARS-CoV-2. IMPORTANCE RNA viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), contain functional structures to support virus replication, translation, and evasion of the host antiviral immune response. The 3' untranslated region of early isolates of SARS-CoV-2 contained a stem-loop II motif (s2m), which is an RNA structural element that is found in many RNA viruses. This motif was discovered over 25 years ago, but its functional significance is unknown. We created SARS-CoV-2 with deletions or mutations of the s2m and determined the effect of these changes on viral growth in tissue culture and in rodent models of infection. Deletion or mutation of the s2m element had no effect on growth in vitro or on growth and viral fitness in Syrian hamsters in vivo. We also observed no impact of the deletion on other known RNA structures in the same region of the genome. These experiments demonstrate that s2m is dispensable for SARS-CoV-2.

Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein

Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

The preference signature of the SARS-CoV-2 Nucleocapsid NTD for its 5'-genomic RNA elements

The nucleocapsid protein (N) of SARS-CoV-2 plays a pivotal role during the viral life cycle. It is involved in RNA transcription and accounts for packaging of the large genome into virus particles. N manages the enigmatic balance of bulk RNA-coating versus precise RNA-binding to designated cis-regulatory elements. Numerous studies report the involvement of its disordered segments in non-selective RNA-recognition, but how N organizes the inevitable recognition of specific motifs remains unanswered. We here use NMR spectroscopy to systematically analyze the interactions of N's N-terminal RNA-binding domain (NTD) with individual cis RNA elements clustering in the SARS-CoV-2 regulatory 5'-genomic end. Supported by broad solution-based biophysical data, we unravel the NTD RNA-binding preferences in the natural genome context. We show that the domain's flexible regions read the intrinsic signature of preferred RNA elements for selective and stable complex formation within the large pool of available motifs.

Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions

RNA pseudoknots are a kind of important tertiary motif, and the structures and stabilities of pseudoknots are generally critical to the biological functions of RNAs with the motifs. In this work, we have carefully refined our previously developed coarse-grained model with salt effect through involving a new coarse-grained force field and a replica-exchange Monte Carlo algorithm, and employed the model to predict structures and stabilities of complex RNA pseudoknots in ion solutions beyond minimal H-type pseudoknots. Compared with available experimental data, the newly refined model can successfully predict 3D structures from sequences for the complex RNA pseudoknots including SARS-CoV-2 programming-1 ribosomal frameshifting element and Zika virus xrRNA, and can reliably predict the thermal stabilities of RNA pseudoknots with various sequences and lengths over broad ranges of monovalent/divalent salts. In addition, for complex pseudoknots including SARS-CoV-2 frameshifting element, our analyses show that their thermally unfolding pathways are mainly dependent on the relative stabilities of unfolded intermediate states, in analogy to those of minimal H-type pseudoknots.

The highly conserved stem-loop II motif is dispensable for SARS-CoV-2

The stem-loop II motif (s2m) is a RNA structural element that is found in the 3' untranslated region (UTR) of many RNA viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Though the motif was discovered over twenty-five years ago, its functional significance is unknown. In order to understand the importance of s2m, we created viruses with deletions or mutations of the s2m by reverse genetics and also evaluated a clinical isolate harboring a unique s2m deletion. Deletion or mutation of the s2m had no effect on growth in vitro , or growth and viral fitness in Syrian hamsters in vivo . We also compared the secondary structure of the 3' UTR of wild type and s2m deletion viruses using SHAPE-MaP and DMS-MaPseq. These experiments demonstrate that the s2m forms an independent structure and that its deletion does not alter the overall remaining 3'UTR RNA structure. Together, these findings suggest that s2m is dispensable for SARS-CoV-2.

IMPORTANCE

RNA viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contain functional structures to support virus replication, translation and evasion of the host antiviral immune response. The 3' untranslated region of early isolates of SARS-CoV-2 contained a stem-loop II motif (s2m), which is a RNA structural element that is found in many RNA viruses. This motif was discovered over twenty-five years ago, but its functional significance is unknown. We created SARS-CoV-2 with deletions or mutations of the s2m and determined the effect of these changes on viral growth in tissue culture and in rodent models of infection. Deletion or mutation of the s2m element had no effect on growth in vitro , or growth and viral fitness in Syrian hamsters in vivo . We also observed no impact of the deletion on other known RNA structures in the same region of the genome. These experiments demonstrate that the s2m is dispensable for SARS-CoV-2.