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RNA helicase proteins perform coupled reactions in which cycles of ATP binding and hydrolysis are used to drive local unwinding of double-stranded RNA (dsRNA). For some helicases in the ubiquitous DEAD-box family, these local unwinding events are integral to folding transitions in structured RNAs, and thus these helicases function as RNA chaperones. An important measure of the efficiency of the helicase-catalyzed reaction is the ATP utilization value, which represents the average number of ATP molecules hydrolyzed during RNA unwinding or a chaperone-assisted RNA structural rearrangement. Here we outline procedures that can be used to measure the ATP utilization value in RNA unwinding or folding transitions. As an example of an RNA folding transition, we focus on the refolding of the Tetrahymena thermophila group I intron ribozyme from a long-lived misfolded structure to its native structure, and we outline strategies for adapting this assay to other RNA folding transitions. For a simple dsRNA unwinding event, the ATP utilization value provides a measure of the coupling between the ATPase and RNA unwinding activities, and for a complex RNA structural transition it can give insight into the scope of the rearrangement and the efficiency with which the helicase uses the energy from ATPase cycles to promote the rearrangement.

Genomic methods have been valuable for identifying RNA-binding proteins (RBPs) and the genes, pathways, and processes they regulate. Nevertheless, standard motif descriptions cannot be used to predict all RNA targets or test quantitative models for cellular interactions and regulation. We present a complete thermodynamic model for RNA binding to the S. cerevisiae Pumilio protein PUF4 derived from direct binding data for 6180 RNAs measured using the RNA on a massively parallel array (RNA-MaP) platform. The PUF4 model is highly similar to that of the related RBPs, human PUM2 and PUM1, with one marked exception: a single favorable site of base flipping for PUF4, such that PUF4 preferentially binds to a non-contiguous series of residues. These results are foundational for developing and testing cellular models of RNA-RBP interactions and function, for engineering RBPs, for understanding the biophysical nature of RBP binding and the evolutionary landscape of RNAs and RBPs.

Posttranslational gene regulation requires a complex network of RNA–protein interactions. Cooperativity, which tunes response sensitivities, originates from protein–protein interactions in many systems. For RNA-binding proteins, cooperativity can also be mediated through RNA structure. RNA structural cooperativity (RSC) arises when binding of one protein induces a redistribution of RNA conformational states that enhance access (positive cooperativity) or block access (negative cooperativity) to additional binding sites. As RSC does not require direct protein–protein interactions, it allows cooperativity to be tuned for individual RNAs, via alterations in sequence that alter structural stability. Given the potential importance of this mechanism of control and our desire to quantitatively dissect features that underlie physiological regulation, we developed a statistical mechanical framework for RSC and tested this model by performing equilibrium binding measurements of the human PUF family protein PUM2. Using 68 RNAs that contain two to five PUM2-binding sites and RNA structures of varying stabilities, we observed a range of structure-dependent cooperative behaviors. To test our ability to account for this cooperativity with known physical constants, we used PUM2 affinity and nearest-neighbor RNA secondary structure predictions. Our model gave qualitative agreement for our disparate set of 68 RNAs across two temperatures, but quantitative deviations arise from overestimation of RNA structural stability. Our results demonstrate cooperativity mediated by RNA structure and underscore the power of quantitative stepwise experimental evaluation of mechanisms and computational tools.

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code., The RNA sequence and secondary structure regulate RNA editing by ADAR. Here the authors employ a CRISPR/Cas9-mediated saturation mutagenesis and machine learning to predict RNA editing efficiency of specific substrates.

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

DEAD-box helicase proteins perform ATP-dependent rearrangements of structured RNAs throughout RNA biology. Short RNA helices are unwound in a single ATPase cycle, but the ATP requirement for more complex RNA structural rearrangements is unknown. Here we measure the amount of ATP used for native refolding of a misfolded group I intron ribozyme by CYT-19, a Neurospora crassa DEAD-box protein that functions as a general chaperone for mitochondrial group I introns. By comparing the rates of ATP hydrolysis and ribozyme refolding, we find that several hundred ATP molecules are hydrolyzed during refolding of each ribozyme molecule. After subtracting nonproductive ATP hydrolysis that occurs in the absence of ribozyme refolding, we find that approximately 100 ATPs are hydrolyzed per refolded RNA as a consequence of interactions specific to the misfolded ribozyme. This value is insensitive to changes in ATP and CYT-19 concentration and decreases with decreasing ribozyme stability. Because of earlier findings that ∼90% of global ribozyme unfolding cycles lead back to the kinetically preferred misfolded conformation and are not observed, we estimate that each global unfolding cycle consumes ∼10 ATPs. Our results indicate that CYT-19 functions as a general RNA chaperone by using a stochastic, energy-intensive mechanism to promote RNA unfolding and refolding, suggesting an evolutionary convergence with protein chaperones.

SummaryHigh-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA/RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

Holliday junctions are critical intermediates in DNA recombination, repair, and restart of blocked replication. Hexapeptides have been identified that bind to junctions and inhibit various junction-processing enzymes, and these peptides confer anti-microbial and anti-tumor properties. Earlier studies suggested that inhibition results from stabilization of peptide-bound Holliday junctions in the square planar conformation. Here, we use single molecule fluorescence resonance energy transfer (smFRET) and two model junctions, which are AT- or GC-rich at the branch points, to show that binding of the peptide KWWCRW induces a dynamic ensemble of junction conformations that differs from both the square planar and stacked X conformations. The specific features of the conformational distributions differ for the two peptide-bound junctions, but both junctions display greatly decreased Mg(2+) dependence and increased conformational fluctuations. The smFRET results, complemented by gel mobility shift and small angle x-ray scattering analyses, reveal structural effects of peptides and highlight the sensitivity of smFRET for analyzing complex mixtures of DNA structures. The peptide-induced conformational dynamics suggest multiple stacking arrangements of aromatic amino acids with the nucleobases at the junction core. This conformational heterogeneity may inhibit DNA processing by increasing the population of inactive junction conformations, thereby preventing the binding of processing enzymes and/or resulting in their premature dissociation.

RNA-guided nucleases (RGNs) provide sequence-specific gene regulation through base-pairing interactions between a small RNA guide and target RNA or DNA. RGN systems, which include CRISPR-Cas9 and RNA interference (RNAi), hold tremendous promise as programmable tools for engineering and therapeutic purposes. However, pervasive targeting of sequences that closely resemble the intended target has remained a major challenge, limiting the reliability and interpretation of RGN activity and the range of possible applications. Efforts to reduce off-target activity and enhance RGN specificity have led to a collection of empirically derived rules, which often paradoxically include decreased binding affinity of the RNA-guided nuclease to its target. We consider the kinetics of these reactions and show that basic kinetic properties can explain the specificities observed in the literature and the changes in these specificities in engineered systems. The kinetic models described provide a foundation for understanding RGN targeting and a necessary conceptual framework for their rational engineering.

Superfamily 2 helicase proteins are ubiquitous in RNA biology and have an extraordinarily broad set of functional roles. Central among these roles are the promotion of rearrangements of structured RNAs and the remodeling of ribonucleoprotein complexes (RNPs), allowing formation of native RNA structure or progression through a functional cycle of structures. Although all superfamily 2 helicases share a conserved helicase core, they are divided evolutionarily into several families, and it is principally proteins from three families, the DEAD-box, DEAH/RHA, and Ski2-like families, that function to manipulate structured RNAs and RNPs. Strikingly, there are emerging differences in the mechanisms of these proteins, both between families and within the largest family (DEAD-box), and these differences appear to be tuned to their RNA or RNP substrates and their specific roles. This review outlines basic mechanistic features of the three families and surveys individual proteins and the current understanding of their biological substrates and mechanisms.

DEAD-box proteins are superfamily 2 helicases that function in all aspects of RNA metabolism. They employ ATP binding and hydrolysis to generate tight, yet regulated RNA binding, which is used to unwind short RNA helices non-processively and promote structural transitions of RNA and RNA-protein substrates. In the last few years, substantial progress has been made toward a detailed, quantitative understanding of the structural and biochemical properties of DEAD-box proteins. Concurrently, progress has been made toward a physical understanding of the RNA rearrangements and folding steps that are accelerated by DEAD-box proteins in model systems. Here, we review the recent progress on both of these fronts, focusing on the mitochondrial DEAD-box proteins Mss116 and CYT-19 and their mechanisms in promoting the splicing of group I and group II introns.

RNA-guided nucleases (RGNs) provide sequence-specific gene regulation through base-pairing interactions between a small RNA guide and target RNA or DNA. RGN systems, which include CRISPR-Cas9 and RNA interference (RNAi), hold tremendous promise as programmable tools for engineering and therapeutic purposes. However, pervasive targeting of sequences that closely resemble the intended target has remained a major challenge, limiting the reliability and interpretation of RGN activity and the range of possible applications. Efforts to reduce off-target activity and enhance RGN specificity have led to a collection of empirically derived rules, which often paradoxically include decreased binding affinity of the RNA-guided nuclease to its target. Here we demonstrate that simple kinetic considerations of the targeting reaction can explain these and other literature observations. The kinetic models described provide a foundation for understanding RGN systems and a necessary physical and functional framework for their rational engineering.

Both scientists and the public would benefit from improved communication of basic scientific research and from integrating scientists into education outreach, but opportunities to support these efforts are limited. We have developed two low-cost programs—"Present Your PhD Thesis to a 12-Year-Old" and "Shadow a Scientist”—that combine training in science communication with outreach to area middle schools. We assessed the outcomes of these programs and found a 2-fold benefit: scientists improve their communication skills by explaining basic science research to a general audience, and students' enthusiasm for science and their scientific knowledge are increased. Here we present details about both programs, along with our assessment of them, and discuss the feasibility of exporting these programs to other universities., The education outreach programs “Present Your PhD Thesis to a 12-Year-Old” and “Shadow a Scientist” provide opportunities for scientists to improve science communication skills and for students to learn about research.

The mitochondrial DEAD-box proteins Mss116p of Saccharomyces cerevisiae and CYT-19 of Neurospora crassa are ATP-dependent helicases that function as general RNA chaperones. The helicase core of each protein precedes a C-terminal extension and a basic tail, whose structural role is unclear. Here we used small-angle X-ray scattering to obtain solution structures of the full-length proteins and a series of deletion mutants. We find that the two core domains have a preferred relative orientation in the open state without substrates, and we visualize the transition to a compact closed state upon binding RNA and adenosine nucleotide. An analysis of complexes with large chimeric oligonucleotides shows that the basic tails of both proteins are attached flexibly, enabling them to bind rigid duplex DNA segments extending from the core in different directions. Our results indicate that the basic tails of DEAD-box proteins contribute to RNA-chaperone activity by binding nonspecifically to large RNA substrates and flexibly tethering the core for the unwinding of neighboring duplexes.

DEAD-box proteins are ubiquitous in RNA-mediated processes and function by coupling cycles of ATP binding and hydrolysis to changes in affinity for single-stranded RNA. Many DEAD-box proteins use this basic mechanism as the foundation for a version of RNA helicase activity, efficiently separating the strands of short RNA duplexes in a process that involves little or no translocation. This activity, coupled with mechanisms to direct different DEAD-box proteins to their physiological substrates, allows them to promote RNA folding steps and rearrangements and to accelerate remodeling of RNA-protein complexes. This review will describe the properties of DEAD-box proteins as RNA helicases and the current understanding of how the energy from ATPase activity is used to drive the separation of RNA duplex strands. It will then describe how the basic biochemical properties allow some DEAD-box proteins to function as chaperones by promoting RNA folding reactions, with a focus on the self-splicing group I and group II intron RNAs.

Interactions between RNA and proteins are pervasive in biology, shaping processes such as mRNA translation, localization, and alternative splicing. Developing a predictive understanding of the energetics of these systems would allow us to model biologically relevant mutations of these interactions and ultimately design novel interactions. Despite recent advances in high throughput experimental technologies that measure the energetics of these systems, quantitative computational prediction of relative RNA/protein binding affinities has remained a challenge. This is partly due to the observation that computational binding affinity prediction methods typically break down when the molecules are highly flexible or undergo significant conformational changes, situations that often arise in RNA/protein binding. Here, we present a novel framework within Rosetta for predicting RNA/protein relative binding affinities that begins to address this issue. Specifically, we show that the nearest neighbor energies, which are typically used for RNA secondary structure prediction, can be used to approximate the unbound free energy of the RNA, thus eliminating the need to explicitly account for the flexibility of the unbound RNA or conformational changes of the RNA upon binding. Using this method of calculating the unbound RNA free energy significantly improves the prediction accuracy over a more typical 3D structure-based approach. We optimized this method using a subset of published MS2 coat protein affinities and ultimately made predictions for the system with 1.11-1.28 kcal/mol root mean square (RMS) error. Additionally, we show that this method is able to predict relative binding affinities for four diverse RNA/protein systems with 1.48 kcal/mol RMS error. Finally, to more rigorously assess this method, we independently measured and made blind predictions for PUF3 and PUM2 binding affinities with RMS errors of 1-2 kcal/mol, which is comparable to the accuracy achieved by prediction methods for other types of systems.

DEAD-box proteins are nonprocessive RNA helicases and can function as RNA chaperones, but the mechanisms of their chaperone activity remain incompletely understood. The Neurospora crassa DEAD-box protein CYT-19 is a mitochondrial RNA chaperone that promotes group I intron splicing and has been shown to resolve misfolded group I intron structures, allowing them to refold. Building on previous results, here we use a series of tertiary contact mutants of the Tetrahymena group I intron ribozyme to demonstrate that the efficiency of CYT-19-mediated unfolding of the ribozyme is tightly linked to global RNA tertiary stability. Efficient unfolding of destabilized ribozyme variants is accompanied by increased ATPase activity of CYT-19, suggesting that destabilized ribozymes provide more productive interaction opportunities. The strongest ATPase stimulation occurs with a ribozyme that lacks all five tertiary contacts and does not form a compact structure, and small-angle X-ray scattering indicates that ATPase activity tracks with ribozyme compactness. Further, deletion of three helices that are prominently exposed in the folded structure decreases the ATPase stimulation by the folded ribozyme. Together, these results lead to a model in which CYT-19, and likely related DEAD-box proteins, rearranges complex RNA structures by preferentially interacting with and unwinding exposed RNA secondary structure. Importantly, this mechanism could bias DEAD-box proteins to act on misfolded RNAs and ribonucleoproteins, which are likely to be less compact and more dynamic than their native counterparts.

RNAs are prone to misfolding, but how misfolded structures are formed and resolved remains incompletely understood. The Tetrahymena group I intron ribozyme folds in vitro to a long-lived misfolded conformation (M) that includes extensive native structure but is proposed to differ in topology from the native state (N). A leading model predicts that exchange of the topologies requires unwinding of the long-range, core helix P3, despite the presence of P3 in both conformations. To test this model, we constructed 16 mutations to strengthen or weaken P3. Catalytic activity and in-line probing showed that nearly all of the mutants form the M state before folding to N. The P3-weakening mutations accelerated refolding from M (3- to 30-fold) and the P3-strengthening mutations slowed refolding (6- to 1400-fold), suggesting that P3 indeed unwinds transiently. Upon depletion of Mg(2+), the mutations had analogous effects on unfolding from N to intermediates that subsequently fold to M. The magnitudes for the P3-weakening mutations were larger than in refolding from M, and small-angle X-ray scattering showed that the ribozyme expands rapidly to intermediates from which P3 is disrupted subsequently. These results are consistent with previous results indicating unfolding of native peripheral structure during refolding from M, which probably permits rearrangement of the core. Together, our results demonstrate that exchange of the native and misfolded conformations requires loss of a core helix in addition to peripheral structure. Further, the results strongly suggest that misfolding arises from a topological error within the ribozyme core, and a specific topology is proposed.

DEAD-box proteins are ubiquitous in RNA-mediated processes and function by coupling cycles of ATP binding and hydrolysis to changes in affinity for single-stranded RNA. Many DEAD-box proteins use this basic mechanism as the foundation for a version of RNA helicase activity, efficiently separating the strands of short RNA duplexes in a process that involves little or no translocation. This activity, coupled with mechanisms to direct different DEAD-box proteins to their physiological substrates, allows them to promote RNA folding steps and rearrangements and to accelerate remodeling of RNA-protein complexes. This review will describe the properties of DEAD-box proteins as RNA helicases and the current understanding of how the energy from ATPase activity is used to drive the separation of RNA duplex strands. It will then describe how the basic biochemical properties allow some DEAD-box proteins to function as chaperones by promoting RNA folding reactions, with a focus on the self-splicing group I and group II intron RNAs.