Cryo-EM reveals an entangled kinetic trap in the folding of a catalytic RNA

Catalytic Relaxation of Kinetically Trapped Intermediates by DNA Chaperones

Common in biomacromolecules, kinetically trapped misfolded intermediates are often detrimental to the structures, properties, or functions of proteins or nucleic acids. Nature employs chaperone proteins but not nucleic acids to escort intermediates to correct conformations. Herein, we constructed a Jablonski-like diagram of a mechanochemical cycle in which individual DNA hairpins were mechanically unfolded to high-energy states, misfolded into kinetically trapped states, and catalytically relaxed back to ground-state hairpins by a DNA chaperone. The capacity of catalytic relaxation was demonstrated in a 1D DNA hairpin array mimicking nanoassembled materials. At ≥1 μM, the diffusive (or self-walking) DNA chaperone converted the entire array of misfolded intermediates to correct conformation in less than 15 s, which is essential to rapidly prepare homogeneous nanoassemblies. Such an efficient self-walking amplification increases the signal-to-noise ratio, facilitating catalytic relaxation to recognize a 1 fM DNA chaperone in 10 min, a detection limit comparable to the best biosensing strategies.

It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs

A novel class of protein misfolding characterized by either the formation of non-native noncovalent lasso entanglements in the misfolded structure or loss of native entanglements has been predicted to exist and found circumstantial support through biochemical assays and limited-proteolysis mass spectrometry data. Here, we examine whether it is possible to design small molecule compounds that can bind to specific folding intermediates and thereby avoid these misfolded states in computer simulations under idealized conditions (perfect drug-binding specificity, zero promiscuity, and a smooth energy landscape). Studying two proteins, type III chloramphenicol acetyltransferase (CAT-III) and D-alanyl-D-alanine ligase B (DDLB), that were previously suggested to form soluble misfolded states through a mechanism involving a failure-to-form of native entanglements, we explore two different drug design strategies using coarse-grained structure-based models. The first strategy, in which the native entanglement is stabilized by drug binding, failed to decrease misfolding because it formed an alternative entanglement at a nearby region. The second strategy, in which a small molecule was designed to bind to a non-native tertiary structure and thereby destabilize the native entanglement, succeeded in decreasing misfolding and increasing the native state population. This strategy worked because destabilizing the entanglement loop provided more time for the threading segment to position itself correctly to be wrapped by the loop to form the native entanglement. Further, we computationally identified several FDA-approved drugs with the potential to bind these intermediate states and rescue misfolding in these proteins. This study suggests it is possible for small molecule drugs to prevent protein misfolding of this type.

All-atom RNA structure determination from cryo-EM maps

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

Regulation of bacterial gene expression by non-coding RNA: It is all about time!

Commensal and pathogenic bacteria continuously evolve to survive in diverse ecological niches by efficiently coordinating gene expression levels in their ever-changing environments. Regulation through the RNA transcript itself offers a faster and more cost-effective way to adapt than protein-based mechanisms and can be leveraged for diagnostic or antimicrobial purposes. However, RNA can fold into numerous intricate, not always functional structures that both expand and obscure the plethora of roles that regulatory RNAs serve within the cell. Here, we review the current knowledge of bacterial non-coding RNAs in relation to their folding pathways and interactions. We posit that co-transcriptional folding of these transcripts ultimately dictates their downstream functions. Elucidating the spatiotemporal folding of non-coding RNAs during transcription therefore provides invaluable insights into bacterial pathogeneses and predictive disease diagnostics. Finally, we discuss the implications of co-transcriptional folding andapplications of RNAs for therapeutics and drug targets.

An RNA excited conformational state at atomic resolution

Sparse and short-lived excited RNA conformational states are essential players in cell physiology, disease, and therapeutic development, yet determining their 3D structures remains challenging. Combining mutagenesis, NMR spectroscopy, and computational modeling, we determined the 3D structural ensemble formed by a short-lived (lifetime ~2.1 ms) lowly-populated (~0.4%) conformational state in HIV-1 TAR RNA. Through a strand register shift, the excited conformational state completely remodels the 3D structure of the ground state (RMSD from the ground state = 7.2 ± 0.9 Å), forming a surprisingly more ordered conformational ensemble rich in non-canonical mismatches. The structure impedes the formation of the motifs recognized by Tat and the super elongation complex, explaining why this alternative TAR conformation cannot activate HIV-1 transcription. The ability to determine the 3D structures of fleeting RNA states using the presented methodology holds great promise for our understanding of RNA biology, disease mechanisms, and the development of RNA-targeting therapeutics.

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Tracking Native Tetrahymena Ribozyme Folding with Fluorescence

Folding of the Tetrahymena group I intron ribozyme and other structured RNAs has been measured using a catalytic activity assay to monitor the native state formation by cleavage of a radiolabeled oligonucleotide substrate. While highly effective, the assay has inherent limitations present in any radioactivity- and gel-based assay. Administrative and safety considerations arise from the radioisotope, and data collection is laborious due to the use of polyacrylamide gels. Here we describe a fluorescence-based, solution assay that allows for more efficient data acquisition. The substrate is labeled with 6-carboxyfluorescein (6FAM) fluorophore and black hole quencher (BHQ1) at the 5' and 3' ends, respectively. Substrate cleavage results in release of the quencher, increasing the fluorescence signal by an average of 30-fold. A side-by-side comparison with the radioactivity-based assay shows good agreement in monitoring Tetrahymena ribozyme folding from a misfolded conformation to the native state, albeit with increased uncertainty. The lower precision of the fluorescence assay is compensated for by the relative ease and efficiency of the workflow. In addition, this assay will allow institutions that do not use radioactive materials to monitor native folding of the Tetrahymena ribozyme, and the same strategy should be amenable to native folding of other ribozymes.

A generalizable scaffold-based approach for structure determination of RNAs by cryo-EM

Single-particle cryo-electron microscopy (cryo-EM) can reveal the structures of large and often dynamic molecules, but smaller biomolecules (≤50 kDa) remain challenging targets due to their intrinsic low signal to noise ratio. Methods to help resolve small proteins have been applied but development of similar approaches to aid in structural determination of small, structured RNA elements have lagged. Here, we present a scaffold-based approach that we used to recover maps of sub-25 kDa RNA domains to 4.5-5.0 Å. While lacking the detail of true high-resolution maps, these maps are suitable for model building and preliminary structure determination. We demonstrate this method helped faithfully recover the structure of several RNA elements of known structure, and that it promises to be generalized to other RNAs without disturbing their native fold. This approach may streamline the sample preparation process and reduce the optimization required for data collection. This first-generation scaffold approach provides a robust system to aid in RNA structure determination by cryo-EM and lays the groundwork for further scaffold optimization to achieve higher resolution.

Watching ion-driven kinetics of ribozyme folding and misfolding caused by energetic and topological frustration one molecule at a time

Folding of ribozymes into well-defined tertiary structures usually requires divalent cations. How Mg2+ ions direct the folding kinetics has been a long-standing unsolved problem because experiments cannot detect the positions and dynamics of ions. To address this problem, we used molecular simulations to dissect the folding kinetics of the Azoarcus ribozyme by monitoring the path each molecule takes to reach the folded state. We quantitatively establish that Mg2+ binding to specific sites, coupled with counter-ion release of monovalent cations, stimulate the formation of secondary and tertiary structures, leading to diverse pathways that include direct rapid folding and trapping in misfolded structures. In some molecules, key tertiary structural elements form when Mg2+ ions bind to specific RNA sites at the earliest stages of the folding, leading to specific collapse and rapid folding. In others, the formation of non-native base pairs, whose rearrangement is needed to reach the folded state, is the rate-limiting step. Escape from energetic traps, driven by thermal fluctuations, occurs readily. In contrast, the transition to the native state from long-lived topologically trapped native-like metastable states is extremely slow. Specific collapse and formation of energetically or topologically frustrated states occur early in the assembly process.

Kinetic Resolution of the Atomic 3D Structures Formed by Ground and Excited Conformational States in an RNA Dynamic Ensemble

Knowing the 3D structures formed by the various conformations populating the RNA free-energy landscape, their relative abundance, and kinetic interconversion rates is required to obtain a quantitative and predictive understanding of how RNAs fold and function at the atomic level. While methods integrating ensemble-averaged experimental data with computational modeling are helping define the most abundant conformations in RNA ensembles, elucidating their kinetic rates of interconversion and determining the 3D structures of sparsely populated short-lived RNA excited conformational states (ESs) remains challenging. Here, we developed an approach integrating Rosetta-FARFAR RNA structure prediction with NMR residual dipolar couplings and relaxation dispersion that simultaneously determines the 3D structures formed by the ground-state (GS) and ES subensembles, their relative abundance, and kinetic rates of interconversion. The approach is demonstrated on HIV-1 TAR, whose six-nucleotide apical loop was previously shown to form a sparsely populated (∼13%) short-lived (lifetime ∼ 45 μs) ES. In the GS, the apical loop forms a broad distribution of open conformations interconverting on the pico-to-nanosecond time scale. Most residues are unpaired and preorganized to bind the Tat-superelongation protein complex. The apical loop zips up in the ES, forming a narrow distribution of closed conformations, which sequester critical residues required for protein recognition. Our work introduces an approach for determining the 3D ensemble models formed by sparsely populated RNA conformational states, provides a rare atomic view of an RNA ES, and kinetically resolves the atomic 3D structures of RNA conformational substates, interchanging on time scales spanning 6 orders of magnitude, from picoseconds to microseconds.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

A Generalizable Scaffold-Based Approach for Structure Determination of RNAs by Cryo-EM

Single-particle cryo-electron microscopy (cryo-EM) can reveal the structures of large and often dynamic molecules, but smaller biomolecules remain challenging targets due to their intrinsic low signal to noise ratio. Methods to resolve small proteins have been applied but development of similar approaches for small structured RNA elements have lagged. Here, we present a scaffold-based approach that we used to recover maps of sub-25 kDa RNA domains to 4.5 - 5.0 Å. While lacking the detail of true high-resolution maps, these are suitable for model building and preliminary structure determination. We demonstrate this method faithfully recovers the structure of several RNA elements of known structure, and it promises to be generalized to other RNAs without disturbing their native fold. This approach may streamline the sample preparation process and reduce the optimization required for data collection. This first-generation scaffold approach provides a system for RNA structure determination by cryo-EM and lays the groundwork for further scaffold optimization to achieve higher resolution.

Structure, folding and flexibility of co-transcriptional RNA origami

RNA origami is a method for designing RNA nanostructures that can self-assemble through co-transcriptional folding with applications in nanomedicine and synthetic biology. However, to advance the method further, an improved understanding of RNA structural properties and folding principles is required. Here we use cryogenic electron microscopy to study RNA origami sheets and bundles at sub-nanometre resolution revealing structural parameters of kissing-loop and crossover motifs, which are used to improve designs. In RNA bundle designs, we discover a kinetic folding trap that forms during folding and is only released after 10 h. Exploration of the conformational landscape of several RNA designs reveal the flexibility of helices and structural motifs. Finally, sheets and bundles are combined to construct a multidomain satellite shape, which is characterized by individual-particle cryo-electron tomography to reveal the domain flexibility. Together, the study provides a structural basis for future improvements to the design cycle of genetically encoded RNA nanodevices.

Snapshots of the second-step self-splicing of Tetrahymena ribozyme revealed by cryo-EM

Group I introns are catalytic RNAs that coordinate two consecutive transesterification reactions for self-splicing. To understand how the group I intron promotes catalysis and coordinates self-splicing reactions, we determine the structures of L-16 Tetrahymena ribozyme in complex with a 5'-splice site analog product and a 3'-splice site analog substrate using cryo-EM. We solve six conformations from a single specimen, corresponding to different splicing intermediates after the first ester-transfer reaction. The structures reveal dynamics during self-splicing, including large conformational changes of the internal guide sequence and the J5/4 junction as well as subtle rearrangements of active-site metals and the hydrogen bond formed between the 2'-OH group of A261 and the N2 group of guanosine substrate. These results help complete a detailed structural and mechanistic view of this paradigmatic group I intron undergoing the second step of self-splicing.

Integrating experimental data with molecular simulations to investigate RNA structural dynamics

Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule Förster resonance energy transfer, or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.

Mobile group I introns at nuclear rDNA position L2066 harbor sense and antisense homing endonuclease genes intervened by spliceosomal introns

Background

Mobile group I introns encode homing endonucleases that confer intron mobility initiated by a double-strand break in the intron-lacking allele at the site of insertion. Nuclear ribosomal DNA of some fungi and protists contain mobile group I introns harboring His-Cys homing endonuclease genes (HEGs). An intriguing question is how protein-coding genes embedded in nuclear ribosomal DNA become expressed. To address this gap of knowledge we analyzed nuclear L2066 group I introns from myxomycetes and ascomycetes.

Results

A total of 34 introns were investigated, including two identified mobile-type introns in myxomycetes with HEGs oriented in sense or antisense directions. Intriguingly, both HEGs are interrupted by spliceosomal introns. The intron in Didymium squamulosum, which harbors an antisense oriented HEG, was investigated in more detail. The group I intron RNA self-splices in vitro, thus generating ligated exons and full-length intron circles. The intron HEG is expressed in vivo in Didymium cells, which involves removal of a 47-nt spliceosomal intron (I-47) and 3′ polyadenylation of the mRNA. The D. squamulosum HEG (lacking the I-47 intron) was over-expressed in E. coli, and the corresponding protein was purified and shown to confer endonuclease activity. The homing endonuclease was shown to cleave an intron-lacking DNA and to produce a pentanucleotide 3′ overhang at the intron insertion site.

Conclusions

The L2066 family of nuclear group I introns all belong to the group IE subclass. The D. squamulosum L2066 intron contains major hallmarks of a true mobile group I intron by encoding a His-Cys homing endonuclease that generates a double-strand break at the DNA insertion site. We propose a potential model to explain how an antisense HEG becomes expressed from a nuclear ribosomal DNA locus.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13100-022-00280-4.

The promise of cryo-EM to explore RNA structural dynamics

Conformational dynamics are essential to macromolecular function. This is certainly true of RNA, whose ability to undergo programmed conformational dynamics is essential to create and regulate complex biological processes. However, methods to easily and simultaneously interrogate both the structure and conformational dynamics of fully functional RNAs in isolation and in complex with proteins have not historically been available. Due to its ability to image and classify single particles, cryogenic electron microscopy (cryo-EM) has the potential to address this gap and may be particularly amenable to exploring structural dynamics within the three-dimensional folds of biologically active RNAs. We discuss the possibilities and current limitations of applying cryo-EM to simultaneously study RNA structure and conformational dynamics, and present one example that illustrates this (as of yet) not fully realized potential.