Kinetic pathway of HIV-1 TAR cotranscriptional folding.

The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.

RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling.

RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.

Machine learning in RNA structure prediction: Advances and challenges.

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

VfoldMCPX: predicting multistrand RNA complexes.

Multistrand RNA complexes play a critical role in RNA-related biological processes. The understanding of RNA functions and the rational design of RNA nanostructures require accurate prediction of the structure and folding stability of the complexes, including those containing pseudoknots. Here, we present VfoldMCPX, a new model for predicting two-dimensional (2D) structures and folding stabilities of multistrand RNA complexes. Based on a partition function-based algorithm combined with physical loop free energy parameters, the VfoldMCPX model predicts not only the native structure but also the folding stability of the complex. An important advantage of the model is the ability to treat pseudoknotted structures. Extensive tests on structure predictions show the VfoldMCPX model provides improved accuracy for multistranded RNA complexes, especially for RNA complexes with three or more strands and/or containing pseudoknots. We have developed a freely accessible VfoldMCPX web server at http://rna.physics.missouri.edu/vfoldMCPX2.

An RNA aptamer that shifts the reduction potential of metabolic cofactors.

The discovery of ribozymes has inspired exploration of RNA's potential to serve as primordial catalysts in a hypothesized RNA world. Modern oxidoreductase enzymes employ differential binding between reduced and oxidized forms of redox cofactors to alter cofactor reduction potential and enhance the enzyme's catalytic capabilities. The utility of differential affinity has been underexplored as a chemical strategy for RNA. Here we show an RNA aptamer that preferentially binds oxidized forms of flavin over reduced forms and markedly shifts flavin reduction potential by -40 mV, similar to shifts for oxidoreductases. Nuclear magnetic resonance structural analysis revealed π-π and donor atom-π interactions between the aptamer and flavin that cause unfavorable contacts with the electron-rich reduced form, suggesting a mechanism by which the local environment of the RNA-binding pocket drives the observed shift in cofactor reduction potential. It seems likely that primordial RNAs could have used similar strategies in RNA world metabolisms.

Advancing RNA 3D structure prediction: Exploring hierarchical and hybrid approaches in CASP15.

In CASP15, we used an integrated hierarchical and hybrid approach to predict RNA structures. The approach involves three steps. First, with the use of physics-based methods, Vfold2D-MC and VfoldMCPX, we predict the 2D structures from the sequence. Second, we employ template-based methods, Vfold3D and VfoldLA, to build 3D scaffolds for the predicted 2D structures. Third, using the 3D scaffolds as initial structures and the predicted 2D structures as constraints, we predict the 3D structure from coarse-grained molecular dynamics simulations, IsRNA and RNAJP. Our approach was evaluated on 12 RNA targets in CASP15 and ranked second among all the 34 participating teams. The result demonstrated the reliability of our method in predicting RNA 2D structures with high accuracy and RNA 3D structures with moderate accuracy. Further improvements in RNA structure prediction for the next round of CASP may come from the incorporation of the physics-based method with machine learning techniques.

Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences.

SUMMARY: RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. AVAILABILITY AND IMPLEMENTATION: http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inhibition of protein arginine methyltransferase 1 alleviates liver fibrosis by attenuating the activation of hepatic stellate cells in mice.

Protein arginine methyltransferase 1 (PRMT1) has been reported to be involved in various diseases. The expression of PRMT1 was increased in cirrhotic livers from human patients. However, the role of PRMT1 in hepatic fibrogenesis remains largely unexplored. In this study, we investigated the effect of PRMT1 on hepatic fibrogenesis and its underlying mechanism. We found that PRMT1 expression was significantly higher in fibrotic livers of the mice treated with thioacetamide (TAA) or 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet. Immunofluorescence staining revealed that PRMT1 expression was augmented in both hepatocytes and hepatic stellate cells (HSCs) in the fibrotic livers. Applying a selective inhibitor of PRMT1, PT1001B, significantly suppressed PRMT1 activity and mitigated liver fibrosis in mice. Hepatocyte-specific Prmt1 knockout did not affect liver fibrosis in mice. PRMT1 overexpression promoted the expression of fibrotic genes in the LX-2 cells, whereas knockdown of PRMT1 or treatment with PT1001B exhibited reversal effects, suggesting that PRMT1 plays an important role in HSC activation. Additionally, HSC-specific Prmt1 knockout attenuated HSC activation and liver fibrosis in TAA-induced fibrotic model. RNA-seq analysis revealed that Prmt1 knockout in HSCs significantly suppressed pro-inflammatory NF-κB and pro-fibrotic TGF-ß signals, and also downregulated the expression of pro-fibrotic mediators in mouse livers. Moreover, treatment with PT1001B consistently inhibited hepatic inflammatory response in fibrotic model. In conclusion, PRMT1 plays a vital role in HSC activation. Inhibition of PRMT1 mitigates hepatic fibrosis by attenuating HSC activation in mice. Therefore, targeting PRMT1 could be a feasible therapeutic strategy for liver fibrosis.

RLDOCK method for predicting RNA-small molecule binding modes.

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

The three-way junction structure of the HIV-1 PBS-segment binds host enzyme important for viral infectivity.

HIV-1 reverse transcription initiates at the primer binding site (PBS) in the viral genomic RNA (gRNA). Although the structure of the PBS-segment undergoes substantial rearrangement upon tRNALys3 annealing, the proper folding of the PBS-segment during gRNA packaging is important as it ensures loading of beneficial host factors. DHX9/RNA helicase A (RHA) is recruited to gRNA to enhance the processivity of reverse transcriptase. Because the molecular details of the interactions have yet to be defined, we solved the solution structure of the PBS-segment preferentially bound by RHA. Evidence is provided that PBS-segment adopts a previously undefined adenosine-rich three-way junction structure encompassing the primer activation stem (PAS), tRNA-like element (TLE) and tRNA annealing arm. Disruption of the PBS-segment three-way junction structure diminished reverse transcription products and led to reduced viral infectivity. Because of the existence of the tRNA annealing arm, the TLE and PAS form a bent helical structure that undergoes shape-dependent recognition by RHA double-stranded RNA binding domain 1 (dsRBD1). Mutagenesis and phylogenetic analyses provide evidence for conservation of the PBS-segment three-way junction structure that is preferentially bound by RHA in support of efficient reverse transcription, the hallmark step of HIV-1 replication.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Spiroarborin, an ent-Clerodane Homodimer from Callicarpa arborea as an Inhibitor of the Eleven-Nineteen Leukemia (ENL) Protein by Targeting the YEATS Domain.

A spiro ent-clerodane homodimer with a rare 6/6/6/6/6-fused pentacyclic scaffold, spiroarborin (1), together with four new monomeric analogues (2-5), were isolated from Callicarpa arborea. Their structures were elucidated by comprehensive spectroscopic data analysis, quantum-chemical calculations, and X-ray diffraction. A plausible biosynthetic pathway of 1 was proposed, and a biomimetic synthesis of its derivative was accomplished. Compound 1 showed a potent inhibitory effect by directly binding to the YEATS domain of the 11-19 leukemia (ENL) protein with an IC50 value of 7.3 µM. This gave a KD value of 5.0 µM, as recorded by a surface plasmon resonance binding assay.

P300/CBP inhibition sensitizes mantle cell lymphoma to PI3Kδ inhibitor idelalisib.

Mantle cell lymphoma (MCL) is a lymphoproliferative disorder lacking reliable therapies. PI3K pathway contributes to the pathogenesis of MCL, serving as a potential target. However, idelalisib, an FDA-approved drug targeting PI3Kδ, has shown intrinsic resistance in MCL treatment. Here we report that a p300/CBP inhibitor, A-485, could overcome resistance to idelalisib in MCL cells in vitro and in vivo. A-485 was discovered in a combinational drug screening from an epigenetic compound library containing 45 small molecule modulators. We found that A-485, the highly selective catalytic inhibitor of p300 and CBP, was the most potent compound that enhanced the sensitivity of MCL cell line Z-138 to idelalisib. Combination of A-485 and idelalisib remarkably decreased the viability of three MCL cell lines tested. Co-treatment with A-485 and idelalisib in Maver-1 and Z-138 MCL cell xenograft mice for 3 weeks dramatically suppressed the tumor growth by reversing the unsustained inhibition in PI3K downstream signaling. We further demonstrated that p300/CBP inhibition decreased histone acetylation at RTKs gene promoters and reduced transcriptional upregulation of RTKs, thereby inhibiting the downstream persistent activation of MAPK/ERK signaling, which also contributed to the pathogenesis of MCL. Therefore, additional inhibition of p300/CBP blocked MAPK/ERK signaling, which rendered maintaining activation to PI3K-mTOR downstream signals p-S6 and p-4E-BP1, thus leading to suppression of cell growth and tumor progression and eliminating the intrinsic resistance to idelalisib ultimately. Our results provide a promising combination therapy for MCL and highlight the potential use of epigenetic inhibitors targeting p300/CBP to reverse drug resistance in tumor.

Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction.

An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.

Structural basis of prostate-specific membrane antigen recognition by the A9g RNA aptamer.

Prostate-specific membrane antigen (PSMA) is a well-characterized tumor marker associated with prostate cancer and neovasculature of most solid tumors. PSMA-specific ligands are thus being developed to deliver imaging or therapeutic agents to cancer cells. Here, we report on a crystal structure of human PSMA in complex with A9g, a 43-bp PSMA-specific RNA aptamer, that was determined to the 2.2 Å resolution limit. The analysis of the PSMA/aptamer interface allows for identification of key interactions critical for nanomolar binding affinity and high selectivity of A9g for human PSMA. Combined with in silico modeling, site-directed mutagenesis, inhibition experiments and cell-based assays, the structure also provides an insight into structural changes of the aptamer and PSMA upon complex formation, mechanistic explanation for inhibition of the PSMA enzymatic activity by A9g as well as its ligand-selective competition with small molecules targeting the internal pocket of the enzyme. Additionally, comparison with published protein-RNA aptamer structures pointed toward more general features governing protein-aptamer interactions. Finally, our findings can be exploited for the structure-assisted design of future A9g-based derivatives with improved binding and stability characteristics.

Binding interface and impact on protease cleavage for an RNA aptamer to HIV-1 reverse transcriptase.

RNA aptamers that bind HIV-1 reverse transcriptase (RT) inhibit RT in enzymatic and viral replication assays. Some aptamers inhibit RT from only a few viral clades, while others show broad-spectrum inhibition. Biophysical determinants of recognition specificity are poorly understood. We investigated the interface between HIV-1 RT and a broad-spectrum UCAA-family aptamer. SAR and hydroxyl radical probing identified aptamer structural elements critical for inhibition and established the role of signature UCAA bulge motif in RT-aptamer interaction. HDX footprinting on RT ± aptamer shows strong contacts with both subunits, especially near the C-terminus of p51. Alanine scanning revealed decreased inhibition by the aptamer for mutants P420A, L422A and K424A. 2D proton nuclear magnetic resonance and SAXS data provided constraints on the solution structure of the aptamer and enable computational modeling of the docked complex with RT. Surprisingly, the aptamer enhanced proteolytic cleavage of precursor p66/p66 by HIV-1 protease, suggesting that it stabilizes the productive conformation to allow maturation. These results illuminate features at the RT-aptamer interface that govern recognition specificity by a broad-spectrum antiviral aptamer, and they open new possibilities for accelerating RT maturation and interfering with viral replication.

Unified energetics analysis unravels SpCas9 cleavage activity for optimal gRNA design.

While CRISPR/Cas9 is a powerful tool in genome engineering, the on-target activity and off-target effects of the system widely vary because of the differences in guide RNA (gRNA) sequences and genomic environments. Traditional approaches rely on separate models and parameters to treat on- and off-target cleavage activities. Here, we demonstrate that a free-energy scheme dominates the Cas9 editing efficacy and delineate a method that simultaneously considers on-target activities and off-target effects. While data-driven machine-learning approaches learn rules to model particular datasets, they may not be as transferrable to new systems or capable of producing new mechanistic insights as principled physical approaches. By integrating the energetics of R-loop formation under Cas9 binding, the effect of the protospacer adjacent motif sequence, and the folding stability of the whole single guide RNA, we devised a unified, physical model that can apply to any cleavage-activity dataset. This unified framework improves predictions for both on-target activities and off-target efficiencies of spCas9 and may be readily transferred to other systems with different guide RNAs or Cas9 ortholog proteins.

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

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Deciphering nucleotide modification-induced structure and stability changes.

Nucleotide modification in RNA controls a bevy of biological processes, including RNA degradation, gene expression, and gene editing. In turn, misregulation of modified nucleotides is associated with a host of chronic diseases and disorders. However, the molecular mechanisms driving these processes remain poorly understood. To partially address this knowledge gap, we used alchemical and temperature replica exchange molecular dynamics (TREMD) simulations on an RNA duplex and an analogous hairpin to probe the structural effects of modified and/or mutant nucleotides. The simulations successfully predict the modification/mutation-induced relative free energy change for complementary duplex formation, and structural analyses highlight mechanisms driving stability changes. Furthermore, TREMD simulations for a hairpin-forming RNA with and without modification provide reliable estimations of the energy landscape. Illuminating the impact of methylated and/or mutated nucleotides on the structure-function relationship and the folding energy landscape, the simulations provide insights into modification-induced alterations to the folding mechanics of the hairpin. The results here may be biologically significant as hairpins are widespread structure motifs that play critical roles in gene expression and regulation. Specifically, the tetraloop of the probed hairpin is phylogenetically abundant, and the stem mirrors a miRNA seed region whose modification has been implicated in epilepsy pathogenesis.

Crystallography-guided discovery of carbazole-based retinoic acid-related orphan receptor gamma-t (RORγt) modulators: insights into different protein behaviors with "short" and "long" inverse agonists.

A series of 6-substituted carbazole-based retinoic acid-related orphan receptor gamma-t (RORγt) modulators were discovered through 6-position modification guided by insights from the crystallographic profiles of the "short" inverse agonist 6. With the increase in the size of the 6-position substituents, the "short" inverse agonist 6 first reversed its function to agonists and then to "long" inverse agonists. The cocrystal structures of RORγt complexed with the representative "short" inverse agonist 6 (PDB: 6LOB), the agonist 7d (PDB: 6LOA) and the "long" inverse agonist 7h (PDB: 6LO9) were revealed by X-ray analysis. However, minor differences were found in the binding modes of "short" inverse agonist 6 and "long" inverse agonist 7h. To further reveal the molecular mechanisms of different RORγt inverse agonists, we performed molecular dynamics simulations and found that "short" or "long" inverse agonists led to different behaviors of helixes H11, H11', and H12 of RORγt. The "short" inverse agonist 6 destabilizes H11' and dislocates H12, while the "long" inverse agonist 7h separates H11 and unwinds H12. The results indicate that the two types of inverse agonists may behave differently in downstream signaling, which may help identify novel inverse agonists with different regulatory mechanisms.