Biology subjects

Herschlag, D.

Publications and source records attributed to Herschlag, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Rho ({rho}) Analysis to Dissect RNA Folding and Assembly Pathways

The modular structure and energetics of RNA simplifies its folding. Leveraging this modularity, we introduce Rho ({rho}) analysis to systematically dissect RNA conformational pathways. {rho} analysis uses isolated RNA secondary or tertiary contacts as external standards to provide insights not possible via the "internal" comparisons of traditional {phi} analysis. Equivalent effects of a mutation on the folding rate constant of the RNA of interest and the thermodynamic stability of the isolated contact indicate that the mutated interaction is fully formed prior to the rate-limiting transition state; the absence of a kinetic effect indicates that the interaction is formed after this transition state. Comparisons with properties of the isolated contact provide additional insights about conformational pathways. We demonstrate {rho} analysis by dissecting Tetrahymena group I intron folding pathways, using a split intron in which the P5abc subdomain assembles with the intron core through three tertiary contacts. We uncover multiple folding pathways and modulation in pathway flux that are readily understood from the energetic properties of the constituent RNA motifs. Extending these concepts to RNA-guided DNA recognition by CRISPR-Cas12a, crRNA-DNA mismatches give substantial {phi} values across much of the target, indicating a late transition state in binding. Thermodynamic penalties from mismatches support modular base-pairing energetics and define an upper bound on DNA target specificity. Our results establish {rho} analysis as a general framework to probe RNA conformational pathways and function. It is straightforward to implement and can be readily applied in vitro and in cells.

biophysics↗

bioRxiv2026

High-throughput biochemical phenotyping of SHP2 variants reveals molecular basis of diseases and allosteric drug inhibition

Interpreting clinical and functional consequences of genetic variants remains challenging due to limited quantitative biochemical data at scale. We applied high-throughput microfluidic enzyme kinetics to profile 190 clinical variants of SHP2, a protein tyrosine phosphatase linked to developmental disorders and cancers. Through >300,000 reaction progress curves, we derived kinetic and thermodynamic parameters quantifying variant effects on catalysis, autoinhibition, stability, phosphopeptide binding, and drug responses. This multidimensional dataset reveals that dysregulated autoinhibition, rather than altered stability or catalysis, predominantly determines SHP2-associated pathogenesis. Thermodynamic modeling reveals that clinical-stage allosteric inhibitors preferentially stabilize a previously underappreciated, partially active conformation over the fully inactive state, leading to variant-dependent drug responses. Our high-throughput biochemical framework establishes a general approach to decipher the biochemical logic connecting protein variants to clinical outcomes.

biochemistry↗

bioRxiv2025

Proteolytic activation of diverse antiviral defense modules in prokaryotes

Linked protease-effector modules are widespread in prokaryotic antiviral defense, yet the mechanisms of most remain poorly understood. Here we show that four of the most prevalent modules--metallo-{beta}-lactamase (MBL)-fold hydrolase, /{beta}-hydrolase, Pepco, and EACC1--form latent death effectors that are unleashed by site-specific proteolysis. Genetic, biochemical, and structural analyses reveal novel modes of effector licensing. MBL and /{beta}-hydrolase are zymogens activated by cleavage at two distinct sites, and upon proteolysis, MBL becomes a Zn2+-dependent double-stranded DNA nuclease. In contrast, Pepco and EACC1 act as pore-forming toxins via distinct mechanisms: Pepco constitutively oligomerizes into a denaturation-resistant beta barrel that is activated by cleavage after a specific isoleucine in its C-terminal tail, whereas EACC1 monomers assemble into large membrane pores following removal of an autoinhibitory domain. All four modules are fused to diverse sensors to detect a wide range of phage signals, and EACC1-DnaK chaperone fusions suggest a convergence between defense and general stress responses. These findings establish proteolysisgated activation as a dominant, modular logic for anti-phage defense and reveal parallels with eukaryotic innate immunity.

molecular biology↗

bioRxiv2025

An Evaluation of Biomolecular Energetics Learned by AlphaFold

Deep learning has revolutionized protein structure prediction, with function prediction on the horizon1,2. Biomolecular properties, including structure and all aspects of function, emerge from atomic-level interactions and the probabilities of their formation3,4. Learning the physical rules that govern these probabilities allows models to deliver accurate structure predictions and may enable extrapolation beyond the training data--a capability needed to predict the many biologically important functional properties where comprehensive data are not readily attainable5. Current structure-based models follow a training logic primarily focused on matching atomic coordinates, rather than atomic interactions and their probabilities. It remains unknown whether the models have learned the physical rules that underlie atomic interactions, the extent of their knowledge, and the prediction errors that arise from limits to this knowledge. We found that state-of-the-art structure prediction models, AlphaFold2, AlphaFold3, and ESMFold, capture basic energetic principles but show pervasive biases in the conformational preferences of molecular interactions. These biases manifest as widespread prediction errors, including the misassignment of a large fraction of side-chain non-covalent interactions--[~]30% for the AlphaFold models and [~]60% for ESMFold--and an inability to reproduce experimentally derived conformational ensembles. More than half the errors made by AlphaFold2 and AlphaFold3 are in common, suggesting limitations not overcome by using different model architectures. Overall, our multifaceted, physics-grounded evaluation identified previously unknown, system-wide deficiencies in current structure prediction models. This framework is applicable to and needed for all biomolecular structure and function prediction models that deliver atomic-level structural information. The insights derived from these evaluations will allow researchers to judiciously apply current models and will guide the development of next-generation models to achieve accurate prediction of biomolecular function.

biophysics↗

bioRxiv2025

Quantifying protein unfolding kinetics with a high-throughput microfluidic platform

Even after folding, proteins transiently sample unfolded or partially unfolded intermediates, and these species are often at risk of irreversible alteration (e.g. via proteolysis, aggregation, or post-translational modification). Kinetic stability, in addition to thermodynamic stability, can directly impact protein lifetime, abundance, and the formation of alternative, sometimes disruptive states. However, we have very few measurements of protein unfolding rates or how mutations alter these rates, largely due to technical challenges associated with their measurement. To address this need, we developed SPARKfold (Simultaneous Proteolysis Assay Revealing Kinetics of Folding), a microfluidic platform to express, purify, and measure unfolding rate constants for >1000 protein variants in parallel via on-chip native proteolysis. To demonstrate the power and potential of SPARKfold, we determined unfolding rate constants for 1,104 protein samples in parallel. We built a library of 31 dihydrofolate reductase (DHFR) orthologs with up to 78 chamber replicates per variant to provide the statistical power required to evaluate the systems ability to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with those obtained using traditional techniques across a 150-fold range, validating the accuracy of the technique. Comparisons of mutant kinetic effects via SPARKfold with previously published measurements impacts on folding thermodynamics provided information about the folding transition state and pathways via {varphi} analysis. Overall, SPARKfold enables rapid characterization of protein variants to dissect the nature of the unfolding transition state. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable rational design of kinetically hyperstable variants for industrial use in harsh environments.

biochemistry↗

bioRxiv2024

SEISMICgraph: a web-based tool for RNA structure data visualization

In recent years, RNA has been increasingly recognized for its essential roles in biology, functioning not only as a carrier of genetic information but also as a dynamic regulator of gene expression through its interactions with other RNAs, proteins, and itself. Advances in chemical probing techniques have significantly enhanced our ability to identify RNA secondary structures and understand their regulatory roles. These developments, alongside improvements in experimental design and data processing, have greatly increased the resolution and throughput of structural analyses. Here, we introduce SEISMICgraph, a web-based tool designed to support RNA structure research by offering data visualization and analysis capabilities for a variety of chemical probing modalities. SEISMICgraph enables simultaneous comparison of data across different sequences and experimental conditions through a user-friendly interface that requires no programming expertise. We demonstrate its utility by investigating known and putative riboswitches and exploring how RNA modifications influence their structure and binding. SEISMICgraphs ability to rapidly visualize adenine-dependent structural changes and assess the impact of pseudouridylation on these transitions provides novel insights and establishes a roadmap for numerous future applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/615187v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1fac718org.highwire.dtl.DTLVardef@1252f03org.highwire.dtl.DTLVardef@31968dorg.highwire.dtl.DTLVardef@1930c4f_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Exploring the energetic and conformational properties of the sequence space connecting naturally occurring RNA tetraloop receptor motifs

Folded RNAs contain tertiary contact motifs whose structures and energetics are conserved across different RNAs. The transferable properties of RNA motifs simplify the RNA folding problem, but measuring energetic and conformational properties of many motifs remains a challenge. Here, we use a high-throughput thermodynamic approach to investigate how sequence changes alter the binding properties of naturally-occurring motifs, the GAAA tetraloop * tetraloop receptor (TLR) interactions. We measured the binding energies and conformational preferences of TLR sequences that span mutational pathways from the canonical 11ntR to two other natural TLRs, the IC3R and Vc2R. While the IC3R and Vc2R share highly similar energetic and conformational properties, the landscapes that map the sequence changes for their conversion from the 11ntR to changes in these properties differ dramatically. Differences in the energetic landscapes stem from the mutations needed to convert the 11ntR to the IC3R and Vc2R rather than a difference in the intrinsic energetic architectures of these TLRs. The conformational landscapes feature several non-native TLR variants with conformational preferences that differ from both the initial and final TLRs; these species represent potential branching points along the multidimensional sequence space to sequences with greater fitness in other RNA contexts with alternative conformational preferences. Our high-throughput, quantitative approach reveals the complex nature of sequence-fitness landscapes and leads to models for their molecular origins. Systematic and quantitative molecular approaches provide critical insights into understanding the evolution of natural RNAs as they traverse complex landscapes in response to selective pressures.

biophysics↗

bioRxiv2024

Conformational Ensembles Reveal the Origins of Serine Protease Catalysis

Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases reveal atomic-level changes across their reaction states, identify molecular features that provide catalysis, and quantify their energetic contributions to catalysis. These enzymes precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. A local catalytic motif, the "nucleophilic elbow", has repeatedly evolved, generating ground state destabilization in 50 proteases and 52 additional enzymes spanning 32 distinct structural folds. Ensemble-function analyses reveal previously unknown catalytic features, provide quantitative models based on simple physical and chemical principles, and identify motifs recurrent in Nature that may inspire enzyme design. One sentence summary: Ensemble-function analyses provide a quantitative model for serine protease catalysis, reveal previously unknown conformational features that contribute to their catalysis, and identify a structural motif that underlie these features and has evolved in >100 different enzymes from 32 protein folds.

biophysics↗

bioRxiv2023

Refinement of Multiconformer Ensemble Models from Multi-temperature X-ray Diffraction Data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

biophysics↗

bioRxiv2022

Dissecting the energetic architecture within an RNA tertiary structural motif via high-throughput thermodynamic measurements

Structured RNAs and RNA/protein complexes perform critical cellular functions. They often contain structurally conserved tertiary contact "motifs," whose occurrence simplifies the RNA folding landscape. Prior studies have focused on the conformational and energetic modularity of intact motifs. Here we turn to the dissection of one common motif, the 11nt receptor (11ntR), using quantitative analysis of RNA on a massively parallel array (RNA-MaP) to measure the binding of all single and double 11ntR mutants to GAAA and GUAA tetraloops, thereby probing the energetic architecture of the motif. While the 11ntR behaves as a motif, its cooperativity is not absolute. Instead, we uncovered a gradient from high cooperativity amongst base paired and neighboring residues to additivity between distant residues. As expected, substitutions at residues in direct contact with the GAAA tetraloop resulted in the largest decreases to binding, and energetic penalties of mutations were substantially smaller for binding to the alternate GUAA tetraloop, which lacks tertiary contacts present with the canonical GAAA tetraloop. However, we found that the energetic consequences of base partner substitutions are not, in general, simply described by base pair type or isostericity. We also found exceptions to the previously established stability-abundance relationship for 11ntR sequence variants. These findings of "exceptions to the rule" highlight the power of systematic high-throughput approaches to uncover novel variants for future study in addition to providing an energetic map of a functional RNA. Significance StatementProperly folded RNAs perform essential biological processes. Many RNAs contain tertiary contact "motifs" whose structural and energetic properties are conserved across different RNAs. This study delves into the nature of an RNA motif. We determined the effects of mutations on the energetic properties of a common tertiary contact motif, referred to as the 11-nucleotide receptor. As deep study of the energetic architecture of this and other RNAs requires thermodynamic measurements for many sequence variants, we used RNA-MaP, a quantitative, high-throughput method, to obtain binding free energies for all single and double mutants of the motif. Our results revealed the energetic architecture of this motif and identified rare variants with unexpected properties.

biophysics↗

bioRxiv2022

RNA conformational propensities determine cellular activity

Cellular processes are the product of interactions between biomolecules, which associate to form biologically active complexes 1. These interactions are mediated by intermolecular contacts, which if disrupted, lead to alterations in cell physiology. Nevertheless, the formation of intermolecular contacts nearly universally requires changes in the conformations of the interacting biomolecules. As a result, binding affinity and cellular activity crucially depend not only on the strength of the contacts, but also on the inherent propensities to form binding-competent conformational states2,3. Thus, conformational penalties are ubiquitous in biology and must be known in order to quantitatively model binding energetics for protein and nucleic acid interactions4,5. However, conceptual and technological limitations have hindered our ability to dissect and quantitatively measure how conformational propensities impact cellular activity. Here, we systematically altered and determined the propensities for forming the protein-bound conformation of HIV-1 TAR RNA. These propensities quantitatively predicted the binding affinities of TAR to the RNA-binding region of the Tat protein and predicted the extent of HIV-1 Tat-dependent transactivation in cells. Our results establish the role of ensemble-based conformational propensities in cellular activity and reveal an example of a cellular process driven by an exceptionally rare and short-lived RNA conformational state.

biophysics↗

bioRxiv2022

High-throughput enzymology reveals mutations throughout a phosphatase that decouple catalysis and transition state analog affinity

Using High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), we measured over 9,000 inhibition curves detailing impacts of 1,004 single-site mutations throughout the Alkaline Phosphatase PafA on binding affinity for two transition state analogs (TSAs), vanadate and tungstate. As predicted by catalytic models invoking transition state complementary, mutations to active site and active site-contacting residues had highly similar impacts on catalysis and TSA binding. Unexpectedly, most mutations to more distal residues which reduced catalysis had little or no impact on TSA binding and many even increased affinity for tungstate. These disparate effects are accounted for by a model in which distal mutations alter the enzymes conformational landscape and increase occupancy of microstates that are catalytically less effective but better able to accommodate larger transition state analogs. In support of this model, glycine substitutions (rather than valine) were more likely to increase tungstate affinity, presumably due to increased conformational flexibility and increased occupancy of previously disfavored microstates. These results indicate that residues throughout an enzyme provide specificity for the transition state and discriminate against analogs that are larger only by tenths of an [A]ngstrom. Thus, engineering enzymes that rival the most powerful natural enzymes will likely require consideration not just of residues in and around the active site, but also of more distal residues that shape the enzymes conformational landscape and finetune the active site. In addition, the extensive functional communication between the active site and remote residues may provide interconnections needed for allostery and make allostery a highly evolvable trait. Significance StatementTransition state analogs (TSAs) resemble fleeting high-energy transition states and have been used to inhibit enzymes in nature and medicine, to learn about enzyme active site features, and to design and select new enzymes. While TSAs mimic transition states, they differ from actual TSs, and we exploit these differences here. Systematic TSA affinity measurements for 1,004 mutants of PafA (a model phosphatase enzyme) revealed effects in and around the active site that mirror their effects on catalysis, but TSA-binding and catalytic effects diverge more distally. These observations suggest that residues throughout an enzyme adjust its conformational landscape on the tenth-[A]ngstrom scale to optimize the active site for catalysis, rendering allostery more evolvable in nature but likely complicating enzyme design.

biochemistry↗

bioRxiv2021

Ensemble-function relationships to evaluate catalysis in the ketosteroid isomerase oxyanion hole

Following decades of insights from structure-function studies, there is now a need to progress from a static to dynamic view of enzymes. Comparison of prior cryo X-ray structures suggested that deleterious effects from ketosteroid isomerase (KSI) mutants arise from misalignment of the oxyanion hole catalytic residue, Y16. However, multi-conformer models from room temperature X-ray diffraction revealed an ensemble of Y16 conformers indistinguishable from WT for Y32F/Y57F KSI and a distinct, non-native ensemble for Y16 in Y57F KSI. Functional analyses suggested rate effects arise from weakened hydrogen bonding, due to disruption of the Y16/Y57/Y32 hydrogen bond network, and repositioning of the general base. In general, catalytic changes can be deconvoluted into effects on the probability of occupying a state (P-effects) and the reactivity of each state (k-effects). Our results underscore the need for ensemble-function analysis to decipher enzyme function and ultimately manipulate their extraordinary capabilities.

biochemistry↗

bioRxiv2021

Systematic investigation of the link between enzyme catalysis and cold adaptation

Cold temperature is prevalent across the biosphere and slows the rates of chemical reactions. Increased catalysis has been predicted to be a general adaptive trait of enzymes to reduced temperature, and this expectation has informed physical models for enzyme catalysis and influenced bioprospecting strategies. To broadly test rate as an adaptive trait to cold, we paired kinetic constants of 2223 enzyme reactions with their organisms optimal growth temperature (TGrowth) and analyzed trends of rate as a function of TGrowth. These data do not support a prevalent increase in rate in cold adaptation. In the model enzyme ketosteroid isomerase (KSI), there was prior evidence for temperature adaptation from a change in an active site residue that results in a tradeoff between activity and stability. Here, we found that little of the overall rate variation for 20 KSI variants was accounted for by TGrowth. In contrast, and consistent with prior expectations, we observed a correlation between stability and TGrowth across 433 proteins. These results suggest that temperature exerts a weaker selection pressure on enzyme rate than stability and that evolutionary forces other than temperature are responsible for the majority of enzymatic rate variation.

biochemistry↗

bioRxiv2021

uPIC-M: efficient and scalable preparation of clonal single mutant libraries for high-throughput protein biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal-Mutant (uPIC-M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC-M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC-M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

molecular biology↗

bioRxiv2021

Damaged goods? Evaluating the impact of X-ray damage on conformational heterogeneity in room temperature and cryo-cooled protein crystals

X-ray crystallography is a cornerstone of biochemistry. Traditional freezing of protein crystals to cryo-temperatures mitigates X-ray damage and facilitates crystal handling but provides an incomplete window into the ensemble of conformations at the heart of protein function and energetics. Room temperature (RT) X-ray crystallography provides more extensive ensemble information, and recent developments allow conformational heterogeneity, the experimental manifestation of ensembles, to be extracted from single crystal data. However, high sensitivity to X-ray damage at RT raises concerns about data reliability. To systematically address this critical question, we obtained increasingly X-ray-damaged high-resolution datasets (1.02-1.52 [A]) from single thaumatin, proteinase K, and lysozyme crystals. Heterogeneity analyses indicated a modest increase in conformational disorder with X-ray damage. Nevertheless, these effects do not alter overall conclusions and can be minimized by limiting the extent of X-ray damage or eliminated by extrapolation to obtain heterogeneity information free from X-ray damage effects. To compare these effects to damage at cryo temperature and to learn more about damage and heterogeneity in cryo-cooled crystals, we carried out an analogous analysis of increasingly damaged proteinase K cryo datasets (0.9-1.16 [A]). We found X-ray damage-associated heterogeneity changes that were not observed at RT. This observation and the scarcity of reported X-ray doses and damage extent render it difficult to distinguish real from artifactual conformations, including those occurring as a function of temperature. The ability to aquire reliable heterogeneity information from single crystals at RT provides strong motivation for further development and routine implementation of RT X-ray crystallography to obtain conformational ensemble information. SignificanceX-ray crystallography has allowed biologists to visualize the proteins that carry out complex biological processes and has provided powerful insights into how these molecules function. Our next level of understanding requires information about the ensemble of conformations that is at the heart of protein function and energetics. Prior results have shown that room temperature (RT) X-ray crystallography provides extensive ensemble information, but are subject to extenstive X-ray damage. We found that ensemble information with little or no effects from X-ray damage can be collected at RT. We also found that damage effects may be more prevalent than recognized in structures obtained under current standard cryogenic conditions. RT X-ray crystallography can be routinely implemented to obtain needed information about conformational ensembles.

biophysics↗

bioRxiv2020

Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK, a microfluidic platform for high-throughput expression, purification, and characterization of >1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA, we performed >670,000 reactions to determine >5000 kinetic and physical constants for multiple substrates and inhibitors. These constants allowed us to uncover extensive kinetic partitioning to a misfolded state and isolate catalytic effects, revealing spatially contiguous "regions" of residues linked to particular aspects of function. These regions included active-site proximal residues but also extended to the enzyme surface, providing a map of underlying architecture that could not be derived from existing approaches. HT-MEK, using direct and coupled fluorescent assays, has future applications to a wide variety of problems ranging from understanding molecular mechanisms to medicine to engineering and design. One Sentence SummaryHT-MEK, a microfluidic platform for high-throughput, quantitative biochemistry, reveals enzyme architectures shaping function.

biochemistry↗

bioRxiv2020

An RNA dynamic ensemble at atomic resolution

Biomolecules do not fold into a single 3D structure but rather form dynamic ensembles of many inter-converting conformations1. Knowledge of dynamic ensembles is key for understanding how biomolecules fold and function, and for rationally manipulating their activities in drug discovery and synthetic biology2-4. However, solving dynamic ensembles of biomolecules at atomic resolution is a major challenge in structural biology because the information required to specify the position of all atoms in thousands of conformations in an ensemble far exceeds the information content of experimental measurements. Here we addressed the data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a conformational library. Library refinement with NMR residual dipolar couplings enabled determination of an atomic-resolution ensemble for HIV-1 TAR as confirmed by quantum-mechanical calculations of NMR chemical shifts, comparison to a crystal structure of a substate, and through the successful redistribution of the ensemble by design using atomic mutagenesis. The ensemble provides an unprecedented view of how bulge residues cooperatively flip out and undergo sugar repuckering to allow the adjoining helices to stack. The generality of this approach will make determination of atomic-resolution RNA ensembles routine.

biophysics↗