An RNA excited conformational state at atomic resolution

Geng, Ainan; Ganser, Laura; Roy, Rohit; Shi, Honglue; Pratihar, Supriya; Case, David A.; Al-Hashimi, Hashim M.

doi:10.1038/s41467-023-43673-6

Download PDF

Article
Open access
Published: 19 December 2023

An RNA excited conformational state at atomic resolution

Nature Communications volume 14, Article number: 8432 (2023) Cite this article

4381 Accesses
2 Citations
5 Altmetric
Metrics details

Subjects

Abstract

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.

Visualizing a protonated RNA state that modulates microRNA-21 maturation

Article 26 October 2020

Rational design of hairpin RNA excited states reveals multi-step transitions

Article Open access 21 March 2022

Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures

Article 02 July 2020

Introduction

With recent breakthroughs in experimental¹ and computational² approaches for determining the atomic three-dimensional (3D) structures formed by the most energetically stable ground states (GSs) of biomolecules, the next challenge in structural biology is to determine the 3D structures formed by short-lived and low-abundance conformational states populating local energetic minima along the free-energy landscape^3,4. These transient, high-energy conformational states, commonly called ‘excited conformational states’ (ESs)⁵, are essential intermediates that form during multistep biochemical reactions, performing functions distinct from those carried out by the more energetically stable GS^3,4. ESs have also been implicated as drivers of various diseases, and some of them are targets for therapeutic development^{3,4,6,7,8,9,10}.

Knowing the 3D structures of ESs is essential for deciphering their biological roles and for the rational design of drugs and other biotechnological applications^3,4. Various methods have been developed to determine the 3D structures of protein ESs, including nuclear magnetic resonance (NMR) spectroscopy^11,12,13,14, room-temperature X-ray crystallography¹⁵, and cryo-electron microscopy (CryoEM)¹. Despite these advancements, including the recent structure determination of a highly populated RNA folding intermediate using CryoEM¹⁶, determining the 3D structures of RNA ESs remains challenging.

Here, we developed a general approach for solving the 3D structural ensemble of RNA ESs, which combines NMR chemical exchange measurements^3,17,18 with targeted mutations stabilizing the minor ES relative to the energetically more stable GS. Our NMR-based approach offers distinct advantages over X-ray crystallography and CryoEM as well as approaches employing ensemble-averaged data^19,20 as it can determine the 3D structures of exceptionally lowly-populated (abundance <1%) and short-lived (lifetime <microsecond) ESs while also measuring their population and lifetime. We developed the approach by determining the conformational ensemble of an ES termed ‘ES2’ formed by the HIV-1 transactivation response element (TAR) RNA^21,22,23. With an exceptionally low population of ~0.4% and a lifetime of ~2.1 ms, the TAR ES2 provides a stringent test for our new methodology.

TAR activates transcription elongation of the HIV-1 retroviral genome by binding to the viral transactivating protein Tat and the super elongation complex (SEC)^24,25,26. While no functional role has yet been assigned to the TAR ES2, point-substitution mutations making ES2 the dominant conformation promote kissing-loop dimerization²², hinting to a potential role in genome dimerization and packaging^27,28,29,30 as well as potently inhibit cellular transactivation possibly pointing to a role in releasing Tat-SEC complex⁹. Regardless of its potential functional roles, the 3D structure of the ES2 is of great interest for the design of anti-HIV therapeutics, which inhibit transcriptional activation by preferentially binding and stabilizing this alternative inactive TAR conformation^9,10.

Strategy for determining conformational ensembles of RNA ESs

Our strategy differs from powerful NMR-based approaches used to determine the 3D structures of protein ESs^11,12,13,14, which rely on the chemical exchange to transfer structural information concerning the NMR-invisible ES to the NMR-visible GS, where it can be readily detected. Instead, our approach builds on the observation that RNA ESs typically form by reshuffling base pairs (bps) in and around non-canonical motifs^{4,18,21,22,31,32,33,34}. Existing NMR methods can determine these alternative secondary structures using chemical shifts, which can be measured even for short-lived, lowly populated states using relaxation dispersion (RD) and chemical exchange saturation transfer (CEST) experiments^{18,21,22,23,31,32,33,34,35,36} (Fig. 1). The alternative secondary structure then guides the design of a mutant RNA construct, which stabilizes the ES relative to the GS, making it the dominant conformation in solution (Fig. 1). Mutations have also been successfully used to stabilize the ESs formed by proteins⁵ and DNA³⁷, illustrating the versatility of the approach.

**Fig. 1: NMR-computational strategy for determining the 3D structural ensemble of excited RNA conformational states.**

The conformational ensemble of the ES-mutant mimic is then determined using Fragment Assembly of RNA with Full-Atom Refinement aided by NMR (FARFAR-NMR)^20,38. In this recently introduced approach²⁰, a conformational library is generated for a given NMR-derived RNA secondary structure using FARFAR structure prediction³⁸ (Fig. 1). The agreement with NMR residual dipolar coupling (RDC)^39,40 data measured for various inter-nuclear bond vectors in the molecule is then used to guide the selection of conformers to be included in an ensemble^39,40,41 (Fig. 1). RDCs measured between two nuclei report on the orientational distribution of bond vectors relative to a molecule-fixed alignment tensor and are ensemble-averaged over all conformations interconverting on the picosecond to millisecond timescales⁴².

Finally, the RDC-optimized ensemble is cross-validated against ¹H, ¹³C, and ¹⁵N chemical shifts, taking advantage of recent advances in quantum mechanical calculations of chemical shifts given an RNA 3D conformational ensemble²⁰ (Fig. 1). This key step employs ensemble-averaged chemical shifts measured directly on the ES in the wild-type (wt) RNA molecule. These chemical shift data are exquisitely sensitive to torsion angle and sugar pucker distributions as well as the propensities of bases to stack and hydrogen bond^20,37,43.

Verifying a mutant mimic of TAR ES2

Utilizing ¹³C and ¹⁵N NMR chemical shifts measured by NMR RD experiments²¹, we previously proposed an alternative secondary structure for the TAR ES2, which forms through a strand-register shift that completely remodels the bulge, upper stem, and apical loop, replacing canonical Watson-Crick bps in the GS with a series of closely spaced mismatches (Fig. 2a). Guided by the secondary structure, we previously^22,23 designed a construct (TAR^ES2), which makes ES2 the dominant conformation by swapping its cUGg_syn apical loop with the much more stable cUUCG_syng loop (Fig. 2b). This TAR^ES2 mutant was shown to adopt the alternative ES2 secondary structure as the dominant conformation^22,23. Moreover, the ¹H, ¹³C, and ¹⁵N chemical shifts measured for the TAR^ES2 mutant were in quantitative agreement (R² = 0.98) with those measured for the transient ES2 in wtTAR^21,23 indicating that it is a good structural mimic of this ES (Fig. 2d).

**Fig. 2: Verifying a mutant mimic of the HIV-1 TAR ES2 using chemical shift fingerprinting.**

To further confirm that the TAR^ES2 mutant does indeed mimic the wtTAR ES2 conformational ensemble, we needed to establish that the bps and non-canonical mismatches observed in the TAR^ES2 mutant also form in the fleeting ES2 (Fig. 2c)³³. To achieve this, we used the recently introduced high-power SELOPE ¹H CEST experiment^44,45 to measure the guanine and uridine imino ¹H chemical shifts of the transient ES2 in wtTAR, as these chemical shifts are highly sensitive to hydrogen-bonding and base-pairing. We then assessed how well the TAR^ES2 mutant reproduces these ES2 chemical shifts.

We observed the expected exchange contributions to the ¹H CEST profiles (Fig. 2e and Supplementary Fig. 1a, b) measured for G26, G28, G36, and U38, all of which reshuffle their bp partners when transitioning from the GS into ES2 (Fig. 2a, highlighted in Fig. 2c). Globally fitting the ¹H CEST profiles to a 2-state exchange model yielded a population (p_ES2 = 0.25 ± 0.01%) and exchange rate (k_ex = k₁ + k_-1 = 737 ± 39 s⁻¹) in very good agreement with values reported previously for ES2 using ¹³C and ¹⁵N RD (p_ES2 = 0.40 ± 0.05% and k_ex = 474 ± 69 s⁻¹) (Fig. 2f). The imino ¹H chemical shifts determined for ES2 using ¹H CEST were in excellent agreement (RMSD = 0.2 ppm) with counterparts measured for the TAR^ES2 mutant (Fig. 2g). These results reinforce the validity of TAR^ES2 as an ES2-mimic and substantiate formation of Watson-Crick G-C, wobble U-U, and two Watson-Crick G_anti-A_anti mismatches in the transient ES2 (Fig. 2a, c), greatly facilitating 3D structure determination. They also establish the utility of high-power ¹H CEST experiment in studying RNA ESs.

Measurement of residual dipolar couplings

Having verified that the TAR^ES2 mutant mimics ES2, we determined its conformational ensemble using FARFAR-NMR^20,38. We measured one-bond ¹³C-¹H (¹D_CH) and ¹⁵N-¹H (¹D_NH) RDCs in Pf1 phage (~17 mg/ml)⁴⁶ in TAR^ES2 as well as on an elongated variant (E-TAR^ES2) in which the lower helix was extended by five bps (Fig. 3a, Supplementary Fig. 2, 3a, Supplementary Table 1). The elongation was used to modulate alignment and to obtain an additional RDC dataset for ensemble determination^47,48. Two independent frequency-based experiments were used to obtain splittings encoded along the ¹³C/¹⁵N or ¹H dimensions⁴⁷, respectively. The root-mean-square-deviation (RMSD) between the two sets of measurements (~2.0 Hz) was used to estimate the RDC uncertainty (Supplementary Fig. 3b).

**Fig. 3: Determining the conformational ensemble of TAR^ES2.**

The RDCs measured for TAR^ES2 differed markedly (RMSD = 14.3 Hz) from counterparts measured in wtTAR, indicating that the ES2 and GS form different conformations (Supplementary Fig. 3c). The similar RDCs measured in TAR^ES2 and E-TAR^ES2 (Supplementary Fig. 3d) and for the two TAR^ES2 helices (Supplementary Fig. 3e) indicated that they are not substantially kinked relative to one another or undergoing large amplitude inter-helical motions across the single uridine bulge. This was in stark contrast to the TAR GS (Supplementary Fig. 3f), in which collective inter-helical motions about the trinucleotide bulge resulted in markedly different RDCs upon helix-elongation as well as differential attenuation of the RDCs measured in the two helices^47,49,50. Thus, the remodeling of junction topology and shortening of the bulge linker appears to alter the TAR global conformation likely reducing the amplitude of inter-helical motions. Nevertheless, the attenuated RDCs and downfield shifted aromatic U23-C6 chemical shift (Supplementary Fig. 2) indicated that the bulge residue U23 remains locally flexible in TAR^ES2.

Determining the ES2 conformational ensemble using FARFAR and RDCs

We used FARFAR-NMR to determine ensembles of the ES-mutant by integrating FARFAR structure prediction with NMR RDC data and then used chemical shifts to cross-validate the generated ensemble. Using FARFAR, we generated a conformational library of N = 10,000 conformers given the NMR-derived TAR^ES2 secondary structure (Fig. 3a). Ensemble averaging over the entire library resulted in poor agreement with the two sets of RDCs; the RMSD of 10.2 Hz substantially exceeded the experimental uncertainty of 2.0 Hz (Fig. 3b and Supplementary Fig. 4a). No single conformer in the FARFAR library satisfied the RDCs and poor agreement was also obtained when ensemble averaging over the ten lowest energy conformations based on the Rosetta energy score (RMSD = 11.2 Hz and R² = 0.71, Fig. 3b and Supplementary Fig. 4b, e) or ten conformations selected randomly (RMSD = 10.7 Hz and R² = 0.74, Fig. 3b and Supplementary Fig. 4c).

Using sample and select (SAS)⁴¹, we used the agreement with the two sets of measured TAR^ES2 RDCs to guide the selection of a subset of conformers from the FARFAR library to form an optimized FARFAR-NMR ensemble. Testing increasingly larger ensemble sizes (N), starting with N = 1 up to N = 49, an optimal ensemble with N = 10 conformers (see Supplementary Fig. 4f) could be obtained, which showed improved RDC agreement across both helices and the bulge. However, despite RDC optimization, the RMSD = 3.3 Hz still exceeded experimental uncertainty (Fig. 3b and Supplementary Fig. 4d). Moreover, cross validation of the FARFAR-NMR ensemble by using the AF-QM/MM^20,43 approach to predict ensemble-averaged chemical shifts (Fig. 3b and Supplementary Figs. 5a–f, 6a–f) revealed that the ensemble poorly predicted the upfield shifted A27-C1’ and A35-C1’ chemical shifts (Fig. 3c), which form tandem Watson-Crick G_anti-A_anti mismatches in TAR^ES2 (Fig. 3a).

Optimizing ensemble using MD

Inspection of the FARFAR ES2 library revealed that it was dominated by conformations in which bulge residue U23 and its neighboring residues (A22, C24, and U25) are intra-helical and stacked, with their sugar moieties primarily adopting the canonical C3’-endo sugar pucker (Supplementary Fig. 7a, b). And yet the attenuated RDCs and downfield shifted U23-C6 chemical shift indicated that the bulge residue is flexible, and a prior analysis of ³J_H1’H2’ scalar couplings and C1’ and C4’ chemical shifts indicated that A22 and U23 significantly sample the non-canonical C2’-endo sugar pucker²³.

FARFAR relies on fragments from the crystallographic database to build RNA structural models³⁸. The unique closely spaced non-canonical motifs found in ES2 may be poorly represented in the PDB and thus difficult to model using this fragment-based approach. Therefore, to increase the conformational diversity and refine the ensemble further, we subjected the ten TAR^ES2 conformers in the optimized FARFAR-NMR ensemble to 600 ns MD simulations using the RNA OL3 force field⁵¹. SAS optimization of the MD-generated conformational library yielded an N = 10 ensemble (FARFAR-MD-NMR) (Supplementary Movie 1), which robustly showed improved agreement with both the RDCs (RMSD = 2.7 Hz) (Fig. 3b, d) and chemical shifts (Fig. 3b, c and Supplementary Figs. 5d, 6d).

The optimized FARFAR-MD-NMR ensemble included conformations in which U23 was flipped out and in which the two helices were coaxially stacked (Fig. 3e). Coaxial stacking of helices coupled to the flipping out of intervening bulge residues (Fig. 3f) is commonly observed in RNAs including in the TAR GS²⁰. In contrast, not only were conformers with U23 flipped out rare in the FARFAR library, but those selected in the RDC optimized FARFAR-NMR ensemble had neighboring bps that were partially melted, and the helices were not coaxially stacked (Fig. 3e). Excluding conformations with U23 flipped out from the FARFAR-MD library reduced the RDC agreement (RMSD = 4.4 Hz) to a level comparable to that of FARFAR-NMR (RMSD = 3.3 Hz) (Supplementary Fig. 7c). Thus, the coaxial conformations with U23 flipped out likely accounted for the improved RDC agreement obtained with the FARFAR-MD versus FARFAR library.

The FARFAR-MD-NMR ensemble also better modeled the tandem G-A mismatches relative to FARFAR-NMR, leading to improved predictions of the A27-C1’ and A35-C1’ chemical shifts (Fig. 3d, Supplementary Figs. 5c–f, 6c–f). A27 and A35 robustly formed tandem Watson-Crick G_anti-A_anti mismatches in the FARFAR-MD-NMR ensemble, whereas some of these mismatches were partially melted in the FARFAR-NMR ensemble (Fig. 3e). In addition, the A27 glycosidic χ-angle was high (−142-146°) for several conformers (Fig. 3e and Supplementary Fig. 7d), in agreement with a prior analysis of the upfield shifted C1’ (but not C4’) ES2 chemical shifts²³, and these conformers were associated with upfield shifted A27-C1’ and A35-C1’, resulting in better agreement with the experimentally measured values (Fig. 3e). Omitting these conformers from the FARFAR-MD-NMR ensemble resulted in poorer agreement with the chemical shifts (Supplementary Fig. 7e, f). High adenosine χ-angles were also previously reported in solution NMR structures of tandem AG mismatches⁵². In contrast, in the FARFAR-NMR ensemble, none of the A27 χ-angles were as high as ~−140° (Fig. 3e).

The FARFAR-MD-NMR ensemble also included alternative secondary structures in which U23 or C24 pair with U40 (Fig. 3g). Due to their propensity to flip out, A22 and U23 were enriched in non-canonical C2’-endo sugar pucker (Supplementary Fig. 7b), in excellent agreement with the sugar pucker distributions, deduced independently using ³J_H1’H2’ scalar couplings and C1’ and C4’ chemical shifts²³. In both the FARFAR-NMR and FARFAR-MD-NMR ensembles, U25-U38 formed alternative wobble conformations, while C24-C39 sampled a wide range of partially paired and unpaired conformations (Fig. 3h) in good agreement with the downfield shifted C24-C6 and C39-C6 chemical shifts and interrupted H8/6-H1’ NOE connectivity at C24-C39 and U25-U38 (Supplementary Figs. 2, 8, 9).

Cross-validating the ensemble using single-atom substitutions

Based on our FARFAR-NMR and FARFAR-MD-NMR ensembles, the tandem AG mismatches adopt the A_anti-G_anti conformation, not other commonly observed conformations such as the A_syn-G_anti Hoogsteen and sheared A-G (Fig. 4a). Thus, our ensemble predicts that replacing the adenosine base with its isosteric base analog 7-deaza-adenosine (c⁷A) that replaces N7 by C7H7 should not impact the formation of the ES2 (Fig. 4a); on the other hand, based on our prior work on DNA⁵³, the modification should destabilize and potentially quench exchange with ES2 if the AG mismatch in ES2 adopted either the A_syn-G_anti Hoogsteen or sheared A-G conformation (Fig. 4a). We tested this prediction using chemical synthesis to prepare wtTAR in which A27 is substituted with c⁷A (Fig. 4b). The G28-H1 and G26-H1 ¹H CEST profiles could be combined in a global fit, yielding downfield shifted G28-H1 (Δω of ~−0.7 ppm) and upfield shifted G26-H1 (Δω of ~0.5 ppm) chemical shifts consistent with the Watson-Crick A_anti-G_anti bps in the wtTAR ES2 (Fig. 4c, d and Supplementary Fig. 10a, c). The exchange rate of k_ex = 614 ± 52 s⁻¹ was in very good agreement with values measured for the wtTAR ES2 (k_ex = 737 ± 39 s⁻¹) using ¹H CEST (Fig. 4d). Indeed, the modification did not quench the exchange rather it increased the population of the ES2 by ~8-fold, possibly due to the destabilization of the GS⁵³. Similar results were obtained robustly for the HIV-2 TAR variant (2U) with the UU dinucleotide bugle (Supplementary Fig. 10b, d).

**Fig. 4: Cross-validating the A27_anti-G36_anti conformation in ES2 using single-atom substitutions.**

ES2 differs substantially from the GS and is more ordered

The TAR ES2 ensemble offered a unique glimpse into the 3D atomic structure formed by a high-energy RNA conformational sub-state (Fig. 5a). This structure diverges significantly from the GS (Fig. 5a), with an average heavy-atom RMSD of 7.2 ± 0.9 Å (Fig. 5b). Not only do the GS and ES2 have distinct secondary structures (Fig. 5a), but they also vary considerably in their global shapes (Fig. 5a). In the GS, the helices adopt a wide range of inter-helical orientations spanning stacked and kinked conformations (Fig. 5a). Inter-helical stacking is accompanied by extra-helical flipping and changes in the sugar pucker distribution at the three bulge residues (Fig. 5b, c). Conversely, in ES2, the two helices consistently maintain co-axial stacking, sampling a much narrower distribution of inter-helical orientations (Fig. 5d), with U23 flipping in and out in a manner coupled to changes in the sugar pucker distribution (Fig. 5c). Thus, the two ensembles exhibit differences one might expect when comparing unrelated RNA sequences.

**Fig. 5: Comparison of the 3D structure and energetics of the TAR GS and ES2.**

It would be reasonable to expect that a high-energy conformational state such as ES2 would lose native contacts and form a broader ensemble distribution relative to the energetically more favored GS. Yet based on heavy-atom RMSD (Fig. 5b), sugar pucker distributions (Fig. 5c), and global inter-helical orientation (Fig. 5d), the ES2 ensemble was much more ordered than the TAR GS (Fig. 5a). Despite being rich in non-canonical mismatches, the upper stem in TAR^ES2 forms a highly structured helix in which the mismatches are base-paired in most conformations. When excluding the C24-C29 terminal bp, the upper stem superimposes with an idealized A-form helix with heavy-atom RMSD of 1.4 ± 0.2 Å (Fig. 5a). On aggregate, TAR^ES2 also has a larger number of bps relative to the GS.

ES2 is entropically disfavored relative to the GS

Our findings raised the question: why is the highly structured ES2 less energetically favorable than the GS? Indeed, many secondary structure prediction programs predict ES2 to be the more stable conformation. To further dissect the relative thermodynamic stabilities of the GS and ES2, we used ¹H CEST experiments to measure the temperature-dependence of the GS to ES2 exchange in wtTAR. Analysis of the temperature-dependent exchange parameters (Fig. 5e, f and Supplementary Fig. 11a) revealed that ES2 is enthalpically favored relative to the GS by ΔH_ES2-GS = −7.7 ± 1.3 kcal/mol (Fig. 5f); a result which we confirmed using ¹⁵N CEST and off-resonance R_1ρ experiments (Supplementary Figs. 11b, c, 13a, b). In addition, analogous experiments on the TAR^ES2 and G36U mutants (Supplementary Figs. 12–14), both of which back exchange with a GS-like conformation¹⁰, yielded oppositely signed ΔH_GS-ES2. Thus, ES2 is less energetically favorable relative to the GS due to the loss of favorable entropy. Although the origins of this entropy difference (e.g., solvent, metal ions, conformation, etc.) remain to be dissected, the loss of conformational entropy when transitioning into the more structured ES2 ensemble could be an important contribution.

Discussion

Previous studies have demonstrated the utility of mutations to stabilize sparsely populated ESs in functionally diverse RNAs¹⁸. These ESs include folding intermediates^32,33, conformations recognized by the microRNA processing machinery³⁴, and inactive conformations, considered attractive drug targets^31,32. The validity of these mutants as ES-mimics could be further verified in the future through extensive measurements of relaxation dispersion data across various nuclei and residues, and this in turn, could provide a route for solving the 3D structures of these other RNA ESs. These applications are needed to test the general applicability of the approach on diverse RNAs. Interestingly, based on secondary structure, many of the ESs formed by other RNAs are also predicted to be more ordered than the GS^31,32,36. Thus, FARFAR-NMR could reveal more intricate and structured conformational states populating higher-energy levels of the RNA-free energy landscape.

Although the TAR^ES2 mutant recapitulated the behavior of the bulge and upper stem in the ES2, it utilized a non-native apical loop to stabilize this high-energy conformation. Consequently, our ensemble did not provide insights into the ES2 apical loop, shown previously to promote TAR dimerization²². The FARFAR-NMR approach could be used in the future to determine the structure of another ES2 mutant, which replaces the G-A mismatch with a U-A bp^9,21 thus preserving the native ES2 apical loop. Alternatively, RDCs⁵⁴ and other structural constraints¹⁴ could be measured directly for the transient ES2 using chemical exchange-based methods⁵⁴ and used in FARFAR-NMR ensemble determination.

The TAR ES2 ensemble highlights the remarkable conformational diversity of motifs rich in non-canonical mismatches commonly observed in RNA ESs¹⁸. Based on a prior structure survey⁵⁵, A-G mismatches flanked by Watson-Crick bps predominantly form the Watson-Crick A_anti-G_anti conformation. Prior NMR structures⁵² have also demonstrated that tandem A-G mismatches within the AG sequence context, flanked by Watson-Crick bps, also form the A_anti-G_anti conformation, with adenosines having high glycosidic angles⁵², but that they can also form A_anti-G_anti or the sheared conformation in the GA sequence context depending the identity of the flanking Watson-Crick bps^56,57. The sheared conformation is also observed robustly in X-ray structures of tandem A-G mismatches in different sequence contexts when they are near non-canonical motifs or near sites forming intermolecular contacts⁵⁸ (Supplementary Table 2). The lack of X-ray structures for tandem A-G mismatches in the AG sequence context, sandwiched by Watson-Crick bps, as occurs in ES2, might explain the canonical glycosidic angles for the adenosines in the FARFAR-generated models (Fig. 3e). In addition to sequence context, the preferences to form alternative A-G conformations can also be modulated by post-transcriptional modifications⁵⁹. Thus, additional studies are needed to dissect the contextual and environmental effects modulating the ensemble behavior of motifs rich in non-canonical mismatches, which appear to be prevalent in RNA ESs.

Our approach for determining conformational ensembles of the ES-stabilizing mutant relies on using state-of-the-art modeling tools including FARFAR and MD simulations to generate an initial library of conformers then using the RDCs to select conformations in the ensemble and chemical shifts to test the ensembles. Compared to conventional structure determination protocols, this approach treats ensemble averaging of the NMR data, uses physical models to address the inherent degeneracies when solving ensembles; and has the advantage of testing state-of-the-art models of RNA structure guiding their future development. In particular, in our prior work on the TAR GS²⁰, we showed that FARFAR does a superior job sampling sugar pucker conformations relative to MD simulations whereas the current work highlights the advantages of using MD in modeling tandem G-A mismatches which may underrepresented in the PDB. However, because RNA ESs such as ES2 are likely to have unusual motifs that are underrepresented in the PDB, it may be helpful and, in some cases, even necessary to pursue full-fledged structure determination and to use the resulting structures as starting points for generating ensembles. Alternatively, multi-conformer refinement approaches could also be used to determine ensembles for the ES-stabilizing mutants^60,61.

Our findings also have important implications for RNA structure prediction and efforts to rationally control and engineer RNA behavior. For TAR, the ES2 was enthalpically favored over the GS, was more structured, and had a larger number of bps and mismatches. The greater entropic stability of GS does not appear to originate from interactions with metal ions, as prior studies showed that adding 1 mM Mg²⁺ minimally affects the TAR GS-ES2 exchange kinetics and thermodynamics⁶². Because GS forms a broader conformational ensemble relative to the ES2, the greater stability of the GS may be driven by conformational entropy. This suggests that a conformational ensemble description may ultimately be required to accurately predict RNA 3D structure and to discriminate the GS from competing ESs.

The non-native highly structured ES2 ensemble explains why it does not support HIV-1 transcriptional activation⁹. Not only is ES2 incapable of forming the base-triple motif required for Tat binding²⁵, but the shape of the apical loop, which contacts both Tat and Cyclin-T1²⁶, is also substantially altered (Fig. 5g) relative to the GS. Moreover, the structure increases the spacing between the bulge and the apical loop, potentially disrupting the simultaneous engagement of Tat and Cylcin-T1 (Fig. 5g). These attributes make ES2 an attractive target for developing anti-HIV therapeutics, which inhibit transcriptional activation.

Thus far, efforts targeting the TAR GS with small molecules have failed to yield potent and selective inhibitors of HIV-1 transcriptional activation⁶³. Not only is it challenging to find compounds that can compete with the Tat-SEC complex for TAR binding, achieving the desired binding selectivity is also difficult because the GS is predominantly composed of canonical Watson-Crick bps, which are abundant in the transcriptome⁶³. On the other hand, Tat-SEC cannot productively bind ES2⁹, and fewer sequences are likely to adopt 3D structures like ES2. Therefore, it may be possible to enhance the selectivity and potency of small molecule inhibitors of TAR by optimizing them to preferentially bind ES2 over the GS. A recent proof-of-concept study demonstrated that a ligand could selectively bind to a sparsely populated (~13%) RNA conformational state and make it the dominant conformation¹⁰. One strategy would involve subjecting the 3D structural ensemble of TAR ES2 and GS²⁰ to virtual screening⁶⁴ and identifying compounds that are predicted to preferentially bind ES2. This approach could be extended to other RNA drug targets that adopt inactive ES conformations^31,32. Thus, the methodology presented here holds great promise in illuminating the functional roles of RNA ESs and advancing methods to exploit them in biotechnological applications.

While no functional role has been assigned to the TAR ES2, we speculate that ES2 could play a role in the dimerization and packaging of the retroviral genome. It has been shown that TAR is required for proper genome dimerization and/or packaging through mechanisms that are not fully understood^27,28,29. In addition, we previously showed that the TAR ES2 has a high propensity to form kissing dimers^9,22. Finally, deletion of the UCU trinucleotide bulge, which inhibits the formation of ES2²¹, significantly impairs retroviral genome dimerization³⁰. These functional roles can be tested in the future by examining the consequence of introducing TAR ES2-stabilizing and ES2-destabilizing mutations in dimerization and packaging assays.

Methods

RNA preparation

Unlabeled wtTAR, TAR^ES2, G36U mutant, c⁷A wtTAR, and c⁷A 2U RNA were synthesized using a MerMade 6 Oligo Synthesizer (BioAutomation) using standard phosphoramidite chemistry and base and 2′-hydroxyl deprotection protocols as described previously⁶². Unlabeled phosphoramidites were purchased from ChemGenes. Uniformly ¹⁵N/¹³C labeled wtTAR, TAR^ES2, and E-TAR^ES2 were prepared by in vitro transcription using T7 RNA polymerase (New England BioLabs), synthetic DNA template (Integrated DNA technologies) containing the T7 promoter sequence (TTAATACGACTCACTATA), and uniformly labeled ¹⁵N/¹³C nucleotides (Cambridge Isotope Laboratories, Inc.). The transcription reaction was carried out at 37 °C for 16 h. All RNAs were purified using a 20% (w/v) denaturing polyacrylamide gel with 8 M urea and 1X TBE (Tris/borate/EDTA). The RNA was extracted from the excised gel by electro-elution (Bio-Rad) followed by concentration and ethanol precipitation. The RNA was then annealed in water at 95 °C for 5 min and snap-cooled on ice for 1 h. Finally, RNA was buffer exchanged using an Amicron Ultra-15 centrifugal filter into NMR buffer (15 mM sodium phosphate, 25 mM sodium chloride, 0.1 mM EDTA and pH 6.4). 10% (v/v) D₂O was added to each sample before NMR data collection. The final concentration of RNA samples ranged between 0.8 and1.4 mM.

NMR experiments

NMR experiments were carried out on Bruker Avance III 600-MHz, Bruker Avance TS2.1 800 MHz, Bruker Avance TS2.1 900 MHz, and NEO 900 MHz spectrometers equipped with 5-mm triple-resonance cryogenic probes. NMR data was analyzed using NMRPipe⁶⁵ and SPARKY (T.D. Goddard and D.G. Kneller, SPARKY 3, University of California, San Francisco). All experiments were performed in NMR buffer with 15 mM sodium phosphate, 25 mM NaCl, 0.1 mM EDTA at pH 6.4 and 10% D₂O, unless stated otherwise.

Resonance assignment

NMR chemical shift assignments for exchangeable and nonexchangeable protons in TAR^ES2 were obtained from prior studies^22,23. To expand and verify these prior resonance assignments, we measured the 2D HCN experiment on labeled TAR^ES2 at 15 °C in 100% D₂O on a 600 MHz Bruker spectrometer equipped with HCN cryogenic probes. This led us to update a few resonance assignments relative to the prior study²² (Supplementary Fig. 9). The resonance originally²² assigned as U25-C6H6 was updated to U38-C6H6. This update did not impact the prior study because no RD measurements were performed on this probe. The resonance originally²² assigned as C39-C6H6 was updated to U25-C6H6 leading to a stronger correlation between the Δω value for U25-C6 deduced from the wt and mutant chemical shifts and the value measured using relaxation dispersion²². Finally, the updated C39-C6H6 resonance is now overlapped with G54-C8H8, which again does not impact our prior study because Δω was never measured for this probe using R_1ρ. The new resonance assignments have been deposited to the BMRB database⁶⁶. In addition, we were able to assign the imino resonances of G28 and G36 in the tandem AG mismatch motif by collecting low temperature (5 °C) 2D ¹H–¹H nuclear Overhauser effect spectroscopy (NOESY) experiment with mixing time of 200 ms (Supplementary Fig. 8a, b).

Measurement of RDCs

One-bond C-H (¹D_CH) and N-H (¹D_NH) RDCs were measured at 25 °C using a 600 MHz Bruker spectrometer equipped with a 5-mm triple-resonance cryogenic probe. C2H2, C6H6, C8H8, C5H5, and C1’H1’ splittings were measured along ¹H dimension using 2D transverse relaxation-optimized spectroscopy (TROSY) experiment and along the ¹³C dimension using the 2D ¹H-¹³C S³CT-heteronuclear single-quantum correlation (HSQC) experiment⁶⁷. N-H (N1H1, N3H3) splittings were measured using 2D ¹H/¹⁵N HSQC experiments in duplicate without decoupling in the indirect (¹⁵N) or direct dimensions (¹H)⁴⁷. RDCs were measured as the difference between splittings obtained in the absence (J) and presence (J + D) of ~17 mg/ml for TAR^ES2 and ~15 mg/ml for E-TAR^ES2 Pf1 phage (Asla biotech, Ltd.) ordering medium⁴⁶. The RDCs used in ensemble determination were the averaged values from the two experiments and the RDC uncertainty was estimated as the RMSD between the two sets of measurements⁴⁷. The measured RDCs of E-TAR^ES2 was scaled down by a normalization factor L_m to account for differences in the degree of alignment between samples.

$${L}_{m}=\frac{\mathop{\sum }\nolimits_{j}{D}_{j}^{{E-{TAR}}^{{ES}2}}\times {D}_{j}^{{{TAR}}^{{ES}2}}}{\mathop{\sum }\nolimits_{j}{D}_{j}^{{{TAR}}^{{ES}2}}\times {D}_{j}^{{{TAR}}^{{ES}2}}}$$

(1)

${{{\mbox{D}}}}_{{{\mbox{j}}}}^{{{{{{{\rm{E}}}}}}-{{{{{\rm{TAR}}}}}}}^{{{{{{\rm{ES}}}}}}2}}$ and ${{{\mbox{D}}}}_{{{\mbox{j}}}}^{{{{{{{\rm{TAR}}}}}}}^{{{{{{\rm{ES}}}}}}2}}$ are the measured RDC of the j^th bond vector for E-TAR^ES2 and TAR^ES2. Measured RDCs are summarized in Supplementary Table 1.

CEST experiment

Temperature-dependent ¹H and ¹⁵N CEST experiments were collected on wtTAR using a 600 MHz Bruker spectrometer equipped with an HCPN cryogenic probe^18,45. Temperature-dependent ¹H CEST experiments measured on TAR^ES2 were collected on an 800 MHz Bruker Avance TS2.1 spectrometer equipped with an HCN cryogenic probe. Temperature-dependent ¹H CEST experiments measured on G36U and c⁷A 2U were collected on a 900 MHz Bruker Avance TS2.1 spectrometer equipped with an HCN cryogenic probe. Temperature-dependent ¹H CEST experiments measured on c⁷A wtTAR were collected on a 900 MHz Bruker Avance NEO/TS4.1 spectrometer equipped with an HCN cryogenic probe. The radiofrequency fields (ω₁2π⁻¹), offsets (Ω2π⁻¹) and mixing time (T_ex) used in the CEST experiments are listed in Supplementary Table 5. The peak intensities at each spin-lock power and offset were extracted using NMRPipe. The experimental uncertainty was obtained based on the standard deviation in peak intensities obtained from triplicate CEST experiments with zero relaxation delay for a given spin-lock power. The radiofrequency fields (RF) field inhomogeneity was measured and accounted for during CEST fitting, as previously described⁴⁵. The exchange parameters of wtTAR, TAR^ES2, and G36U summarized in Supplementary Table 3 were obtained by fitting the normalized intensity data to a two-state Bloch-McConnel equation using an in-house Python script^18,45. c⁷A wtTAR and c⁷A 2U were subjected to three-state fits with triangular topologies that simultaneously detect exchange between multiple excited states (B and C where B corresponds to ES2). The fitted parameters are summarized in Supplementary Table 4. The errors in exchange parameters were set to the standard error (SEM) derived from the square root of the diagonal elements in the covariance matrix of the fitted parameters. The ¹H CEST profiles were also fit with and without (p_ES = k_ES = Δω = 0) exchange. Model selection for fits with and without exchange was performed as previously described¹⁸ by computing Akaike (wAIC) and Bayesian information criterion (wBIC).

Off-resonance R_1p relaxation dispersion

Off-resonance ¹⁵N R_1p experiments were collected on wtTAR using a 600 MHz Bruker AVANCE-III spectrometer equipped with a triple channel cryogenic probe at 35 °C¹⁸. The spin-lock powers (ω2π⁻¹), offsets (Ω2π⁻¹), and delay time used in R_1ρ experiments are listed in Supplementary Table 6. The peak intensity at each relaxation delay was extracted using NMRPipe and fitted to a monoexponential decay using an in-house python script⁶⁸. Bloch-McConnel equations were employed to fit the off-resonance R_1ρ value to a two-state exchange model to determine the exchange rate (k_ex), ES population (p_ES) and the difference between the ES and GS chemical shifts (Δω = ω_ES – ω_GS). The fitting errors were estimated using a Monte Carlo approach with 500 iterations¹⁸.

FARFAR-NMR

Generating ensembles using FARFAR

TAR^ES2 conformational library (N = 100,000) was generated using FARFAR. FARFAR is implemented as the rna_denovo program in the Rosetta Software Suite, which requires RNA sequence and optional secondary structure as input. Non-terminal residues in the lower helix (G18-C44, C19-G43, A20-U42, and G21-C41) were modeled as canonical Watson–Crick bps and imposed to a FARFAR generated idealized A form helix to reduce the run time²⁰. No constraints were applied to junctional residues in and around the bulge including U23, A22-U40 which based on the U40-H3 imino proton form a labile bp, and C24-C39 for which we could not obtain any evidence for base-pairing. Pairing constraints were applied to all other bps in the upper helix including G26-C37, A27-G36, G28-A35, C29-G34, and C30-G33 for which imino resonances consistent with base pairing were observed in 2D NOESY spectra (Supplementary Fig. 8) but without specifying the bp geometry. More specific NMR derived base-pairing constraints were applied to U25-U38 (paired via the Watson-Crick face) and U51-G54(syn) (trans wobble) given NOE-based distance connectivity establishing the dominant paired geometry of these bps. The FARFAR input files and commands were summarized in Supplementary Table 7. The initially generated 100,000 structures were subjected to a Rosetta energy unit ≤ 0 filter to remove models that potentially have chain breaks and severe steric clashes, after which 10,000 conformers were randomly selected to form the final conformational library. The corresponding library for E-TAR^ES2 was obtained by elongating the lower helix in the TAR^ES2 conformers by superimposing an idealized A-form helix before RDC calculation^20,48.

RDC calculations

RDCs were calculated using the program PALES⁶⁹ for each conformer in an ensemble. The RDC values were then averaged over all conformers in the ensemble assuming equal probability. Separate scaling factors were applied to the predicted RDCs of TAR^ES2 and E-TAR^ES2 to account for differences in the degree of alignment between samples²⁰.

Sample and select (SAS)

We employed the SAS approach⁴¹ to generate RDC-satisfying ensembles from a library. Briefly, a simulated annealing Monte Carlo sampling scheme was used to select N conformers (without replacement) that minimizes the differences between the measured and predicted RDCs, in which N represents the number of conformers in the ensemble or ensemble size. The effective starting temperature for simulated annealing was 100 and decreased by a factor of 0.9 in every step for a total of 5 × 10⁶ steps. The agreement between predicted and measured RDCs is evaluated using the cost function:

$${\chi }^{2}=\frac{\mathop{\sum }\nolimits_{j}\left(L\times {D}_{j}^{{pred}}-{D}_{j}^{{meas}}\right)^{2}}{{N}_{{RDC}}}$$

(2)

D_j^pred and D_j^meas are the predicted and measured RDC of the j^th bond vector, respectively; L is the scaling factor; and N_RDC is the total number of bond vectors. The optimal ensemble size was obtained by repeating SAS with increasing ensemble size (from N = 1 to 50) and finding an ensemble size at which the RDC RMSD reaches a plateau²⁰.

Molecular dynamics simulations

The FARFAR-MD library was generated by running MD simulations for each of the 10 conformers in the FARFAR-NMR ensemble as starting structures using the RNA OL3 force field⁵¹ as recommended in the AMBER MD simulation package. Starting structures were solvated with 12 Å buffer of water⁷⁰, and were then neutralized by adding Na⁺ ions. The equilibration phase of the simulation was performed in two steps. First 300 ns of equilibration was carried out with gradually diminishing restraints to the starting structures, allowing the system to relax and reach a stable conformation. This was followed by 600 ns of production NVT simulations using a Langevin thermostat with a collision frequency of 5 ps and a time-step of 2 fs to generate 300 snapshots per starting structure. Taken together, these simulations for all ten structures correspond to a total computational time for equilibration of ~160 h and production simulation of ~400 h on a single Titan V GPU. The final FARFAR-MD library was generated by combining the 10 starting FARFAR structures with the 3000 structures generated through MD simulation.

Automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) chemical shift calculations

Automated Fragmentation quantum mechanical calculation of NMR chemical shifts (AFNMR) software⁴³ was used to calculate ensemble chemical shift as described previously²⁰. For each RNA conformer in the ensemble, a series of five conjugate gradient energy minimization steps on heavy atoms were performed with 2 kcal/mol Å² harmonic restraints to regularize bond lengths and minimize noise in predictions. Each residue was broken into quantum mechanical fragments with a full quantum mechanical representation of heavy-atoms using a distance cutoff of 3.4 Å. The RNA atoms located outside the quantum region, water and ions present in the solvent were assigned as point charges uniformly distributed on the molecular surface. These charges were then resolved by fitting to Poisson−Boltzmann calculations (solinprot from MEAD⁷¹). A local dielectric constant (ε) of 1, 4 and 80 were assigned to the quantum core, regions occupied by the conformer outside the core and the solvent, respectively. GIAO-DFT calculations in Orca5⁷² (version 5.0.4) were carried out for each fragment, using the OLYP⁷³ functional and pcSseg-1 (triple-z plus polarization) basis set optimized for NMR shielding⁷⁴. The predicted chemical shifts obtained from the isotropic components of the computed shielding tensor were referenced using reference shielding computations on tetramethylsilane (TMS). A linear correction was applied to the predicted chemical shifts in a nucleus type-dependent manner²⁰.

Ensemble analysis

The visualization of all ensembles was carried out using PyMOL (https://pymol.org/). All bp geometries, backbone, stacking, and sugar dihedral angles were calculated using X3DNA-DSSR⁷⁵. The inter-helical Euler angles (α_h, β_h, γ_h) were computed by superimposing idealized A-form geometry on three consecutive bps (lower helix: C19-G43, A20-U42, G21-C41; upper helix: G26-C37, A27-G36, G28-A35) and computing the relative orientation between these two helices²⁰. Conformers with U23 flipped in or out in the FARFAR-NMR and FARFAR-MD-NMR ensembles (both N = 10) were identified by visual examination. For the FARFAR-MD-Library (N = 3010), a heavy-atom RMSD filter was used to examine the impact of excluding conformations with U23 flipped out on the RDC agreement in higher throughput. First, pairwise heavy-atom RMSD was calculated in the FARFAR-Random ensemble using the rms2d command in the CPPTRAJ suite⁷⁶, and the conformer with the smallest overall RMSD to remaining conformers was selected as the reference. Next, pairwise heavy-atom RMSD for the bulge motif (A22, U23, C24, C39 and U40) was calculated for all conformers (N = 3010) in FARFAR-MD-Library relative to the reference. Conformers with RMSD > 3.4 Å predominantly had U23 flipped out and were thus filtered out from the library.

The χ-angle of A27 and A35 in FARFAR-NMR ensemble were set to −130° using PyMOL as follow:

cmd.set_dihedral(“resi 27 and name O4’“, “resi 27 and name C1’“, “resi 27 and name N9”, “resi 27 and name C4”, −130)

cmd.set_dihedral(“resi 35 and name O4’“, “resi 35 and name C1’“, “resi 35 and name N9”, “resi 35 and name C4”, −130)

Conformers with steric clashes introduced by this χ-angle adjustment were identified by visual inspection and restored to their original values.

Survey of A-G mismatches in the PDB

All X-ray structures with a resolution of ≤3.0 Å (including unbound RNA, RNA–protein complexes and so on) were downloaded from RCSB Protein Data Bank (PDB) on July 2021 and analyzed using X3DNA-DSSR⁷⁵ to generate a JSON file library. An in-house python script was used to parse the data and create a searchable database containing RNA structural information. Tandem AG/GA bps were identified as sequentially numbered A-G mismatches in the PDB. A total of 384 tandem AG mismatches, corresponding to 36 unique bps, were identified from 117 X-ray crystal structures. Out of these, we examined 11 representative structures that corresponded to 26 unique bps (Supplementary Table 2).

Thermodynamic analysis

A modified van’t Hoff equation was used to fit the observed temperature dependence of the forward (k₁) and reverse (k₋₁) rate constants measured using ¹H CEST, ¹⁵N CEST, and ¹⁵N R_1ρ (Supplementary Table 3). This equation accounts for statistical compensation effects and assumes a smooth energy surface^18,45,77.

$${{{{\mathrm{ln}}}}}\left(\frac{{k}_{i}(T)}{T}\right)={{{{\mathrm{ln}}}}}\left(\frac{{k}_{B}{{{{{\rm{\kappa }}}}}}}{h}\right)-\frac{\Delta {G^\circ }_{i}^{T}({T}_{{hm}})}{R{T}_{{hm}}}-\frac{\Delta {H^\circ }_{i}^{T}}{R}\left(\frac{1}{T}-\frac{1}{{T}_{{hm}}}\right)$$

(3)

where ${k}_{i}$ (i = 1, −1) is the forward and reverse rate constant computed as ${k}_{1}={k}_{{ex}}{p}_{{ES}}$ and ${k}_{-1}={k}_{{ex}}{p}_{{GS}}$, $\Delta {G^\circ }_{i}^{T}$ and $\Delta {H^\circ }_{i}^{T}$ are the free energy and enthalpy of activation, ${k}_{B}$ is Boltzmann’s constant, $h$ is Plank’s constant, κ is the transmission coefficient (assumed to be 1), $R$ is the universal gas constant, $T$ is the temperature, and ${{T}}_{{hm}}$ is the harmonic mean of the experimental temperatures calculated as ${T}_{{hm}}=n/({\sum }_{i=1}^{n}{({1}/{T}_{i}))}$. The entropy of activation ($\Delta {S^\circ }_{i}^{T}$) was computed using the free energy and enthalpy obtained above:

$${T}_{{hm}}\Delta {S^\circ }_{i}^{T}=\Delta {H^\circ }_{i}^{T}-\Delta {G^\circ }_{i}^{T}({T}_{{hm}})$$

(4)

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available from the corresponding authors upon request. The NMR data generated in this study are included in the published article and the Supplementary Information file and have been deposited in the BMRB database under accession code 31106 [https://doi.org/10.13018/BMR31106]. The FARFAR-MD-NMR ensemble model of TAR^ES2 (N = 10) used in this study is available in the PDB database under accession code 8U3M. All raw data and structural models are available on GitHub at https://github.com/alhashimilab/TAR_ES2_ensemble.

Code availability

The Rosetta software suite is available at https://www.rosettacommons.org/software/academic. The AFNMR programs are available at https://github.com/dacase/afnmr. Custom in-house Python scripts for running sample and selection are available at https://github.com/alhashimilab/PySAS. Custom in-house Python scrips for the calculation of inter-helical Euler angles are available at https://github.com/alhashimilab/ABG_calc. Custom in-house Python scrips for the ¹H CEST data fitting and thermodynamic analysis are available at https://github.com/alhashimilab/TAR_ES2_ensemble.

References

Chen, B. & Frank, J. Two promising future developments of cryo-EM: capturing short-lived states and mapping a continuum of states of a macromolecule. Microscopy 65, 69–79 (2016).
Article PubMed Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Article PubMed PubMed Central ADS CAS Google Scholar
Alderson, T. R. & Kay, L. E. NMR spectroscopy captures the essential role of dynamics in regulating biomolecular function. Cell 184, 577–595 (2021).
Article PubMed CAS Google Scholar
Ganser, L. R., Kelly, M. L., Herschlag, D. & Al-Hashimi, H. M. The roles of structural dynamics in the cellular functions of RNAs. Nat. Rev. Mol. Cell Biol. 20, 474–489 (2019).
Article PubMed PubMed Central CAS Google Scholar
Mulder, F. A. A., Mittermaier, A., Hon, B., Dahlquist, F. W. & Kay, L. E. Studying excited states of proteins by NMR spectroscopy. Nat. Struct. Biol. 8, 932–935 (2001).
Article PubMed CAS Google Scholar
Xie, T., Saleh, T., Rossi, P. & Kalodimos, C. G. Conformational states dynamically populated by a kinase determine its function. Science 370, eabc2754 (2020).
Clore, G. M. NMR spectroscopy, excited states and relevance to problems in cell biology–transient pre-nucleation tetramerization of huntingtin and insights into Huntington’s disease. J. Cell Sci. 135, jcs258695 (2022).
Article PubMed PubMed Central CAS Google Scholar
Dallaire, P. et al. Structural dynamics control the MicroRNA maturation pathway. Nucleic Acids Res. 44, 9956–9964 (2016).
PubMed PubMed Central Google Scholar
Ganser, L. R. et al. Probing RNA conformational equilibria within the functional cellular context. Cell Rep. 30, 2472–2480.e4 (2020).
Article PubMed PubMed Central CAS Google Scholar
Ganser, L. R., Kelly, M. L., Patwardhan, N. N., Hargrove, A. E. & Al-Hashimi, H. M. Demonstration that small molecules can bind and stabilize low-abundance short-lived RNA excited conformational states. J. Mol. Biol. 432, 1297–1304 (2020).
Article PubMed CAS Google Scholar
Vallurupalli, P., Hansen, D. F. & Kay, L. E. Structures of invisible, excited protein states by relaxation dispersion NMR spectroscopy. Proc. Natl Acad. Sci. USA 105, 11766–11771 (2008).
Article PubMed PubMed Central ADS CAS Google Scholar
Korzhnev, D. M., Religa, T. L., Banachewicz, W., Fersht, A. R. & Kay, L. E. A transient and low-populated protein-folding intermediate at atomic resolution. Science 329, 1312–1316 (2010).
Article PubMed ADS CAS Google Scholar
Tang, C., Iwahara, J. & Clore, G. M. Visualization of transient encounter complexes in protein-protein association. Nature 444, 383–386 (2006).
Article PubMed ADS CAS Google Scholar
Stiller, J. B. et al. Structure determination of high-energy states in a dynamic protein ensemble. Nature 603, 528–535 (2022).
Article PubMed PubMed Central ADS CAS Google Scholar
Fraser, J. S. et al. Accessing protein conformational ensembles using room-temperature X-ray crystallography. Proc. Natl Acad. Sci. USA 108, 16247–16252 (2011).
Article PubMed PubMed Central ADS CAS Google Scholar
Bonilla, S. L., Vicens, Q. & Kieft, J. S. Cryo-EM reveals an entangled kinetic trap in the folding of a catalytic RNA. Sci. Adv. 8, eabq4144 (2022).
Article PubMed PubMed Central ADS CAS Google Scholar
Palmer, A. G. III Chemical exchange in biomacromolecules: past, present, and future. J. Magn. Reson 241, 3–17 (2014).
Article PubMed PubMed Central ADS CAS Google Scholar
Rangadurai, A., Szymaski, E. S., Kimsey, I. J., Shi, H. & Al-Hashimi, H. M. Characterizing micro-to-millisecond chemical exchange in nucleic acids using off-resonance R1ρ relaxation dispersion. Prog. Nucl. Magn. Reson Spectrosc. 112-113, 55–102 (2019).
Article PubMed PubMed Central CAS Google Scholar
Roy, R. et al. Kinetic resolution of the atomic 3D structures formed by ground and excited conformational states in an RNA dynamic ensemble. J. Am. Chem. Soc. 145, 22964–22978 (2023).
Article PubMed CAS Google Scholar
Shi, H. et al. Rapid and accurate determination of atomistic RNA dynamic ensemble models using NMR and structure prediction. Nat. Commun. 11, 5531 (2020).
Article PubMed PubMed Central ADS CAS Google Scholar
Lee, J., Dethoff, E. A. & Al-Hashimi, H. M. Invisible RNA state dynamically couples distant motifs. Proc. Natl Acad. Sci. USA 111, 9485–9490 (2014).
Article PubMed PubMed Central ADS CAS Google Scholar
Merriman, D. K. et al. Shortening the HIV-1 TAR RNA bulge by a single nucleotide preserves motional modes over a broad range of time scales. Biochemistry 55, 4445–4456 (2016).
Article PubMed CAS Google Scholar
Clay, M. C., Ganser, L. R., Merriman, D. K. & Al-Hashimi, H. M. Resolving sugar puckers in RNA excited states exposes slow modes of repuckering dynamics. Nucleic Acids Res. 45, e134–e134 (2017).
Article PubMed PubMed Central CAS Google Scholar
Frankel, A. D. Activation of HIV transcription by Tat. Curr. Opin. Genet Dev. 2, 293–298 (1992).
Article PubMed CAS Google Scholar
Puglisi, J. D., Chen, L., Frankel, A. D. & Williamson, J. R. Role of RNA structure in arginine recognition of TAR RNA. Proc. Natl Acad. Sci. USA 90, 3680–3684 (1993).
Article PubMed PubMed Central ADS CAS Google Scholar
Chavali, S. S., Bonn-Breach, R. & Wedekind, J. E. Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery. J. Biol. Chem. 294, 9326–9341 (2019).
Article PubMed PubMed Central CAS Google Scholar
Harrich, D., Hooker, C. W. & Parry, E. The human immunodeficiency virus type 1 TAR RNA upper stem-loop plays distinct roles in reverse transcription and RNA packaging. J. Virol. 74, 5639–5646 (2000).
Article PubMed PubMed Central CAS Google Scholar
Das, A. T., Vrolijk, M. M., Harwig, A. & Berkhout, B. Opening of the TAR hairpin in the HIV-1 genome causes aberrant RNA dimerization and packaging. Retrovirology 9, 1–12 (2012).
Article Google Scholar
Andersen, E. S. et al. Role of the trans-activation response element in dimerization of HIV-1 RNA. J. Biol. Chem. 279, 22243–22249 (2004).
Article PubMed CAS Google Scholar
Jalalirad, M., Saadatmand, J. & Laughrea, M. Dominant role of the 5′ TAR bulge in dimerization of HIV-1 genomic RNA, but no evidence of TAR–TAR kissing during in vivo virus assembly. Biochemistry 51, 3744–3758 (2012).
Article PubMed CAS Google Scholar
Chu, C. C., Plangger, R., Kreutz, C. & Al-Hashimi, H. M. Dynamic ensemble of HIV-1 RRE stem IIB reveals non-native conformations that disrupt the Rev-binding site. Nucleic Acids Res. 47, 7105–7117 (2019).
Article PubMed PubMed Central CAS Google Scholar
Dethoff, E. A., Petzold, K., Chugh, J., Casiano-Negroni, A. & Al-Hashimi, H. M. Visualizing transient low-populated structures of RNA. Nature 491, 724–728 (2012).
Article PubMed PubMed Central ADS CAS Google Scholar
Xue, Y., Gracia, B., Herschlag, D., Russell, R. & Al-Hashimi, H. M. Visualizing the formation of an RNA folding intermediate through a fast highly modular secondary structure switch. Nat. Commun. 7, 1–11 (2016).
Article Google Scholar
Baronti, L. et al. Base-pair conformational switch modulates miR-34a targeting of Sirt1 mRNA. Nature 583, 139–144 (2020).
Article PubMed ADS CAS Google Scholar
Zhao, B., Guffy, S. L., Williams, B. & Zhang, Q. An excited state underlies gene regulation of a transcriptional riboswitch. Nat. Chem. Biol. 13, 968–974 (2017).
Article PubMed PubMed Central CAS Google Scholar
Chen, B., LeBlanc, R. & Dayie, T. K. SAM-II Riboswitch Samples at least Two Conformations in Solution in the Absence of Ligand: Implications for Recognition. Angew. Chem. Int Ed. Engl. 55, 2724–2727 (2016).
Article PubMed CAS Google Scholar
Shi, H. et al. Atomic structures of excited state A-T Hoogsteen base pairs in duplex DNA by combining NMR relaxation dispersion, mutagenesis, and chemical shift calculations. J. Biomol. NMR 70, 229–244 (2018).
Article PubMed PubMed Central CAS Google Scholar
Watkins, A. M., Rangan, R. & Das, R. FARFAR2: improved de novo rosetta prediction of complex global RNA folds. Structure 28, 963–976.e6 (2020).
Article PubMed PubMed Central CAS Google Scholar
Tolman, J. R., Flanagan, J. M., Kennedy, M. A. & Prestegard, J. H. Nuclear magnetic dipole interactions in field-oriented proteins: information for structure determination in solution. Proc. Natl Acad. Sci. USA 92, 9279–9283 (1995).
Article PubMed PubMed Central ADS CAS Google Scholar
Tjandra, N. & Bax, A. Direct measurement of distances and angles in biomolecules by NMR in a dilute liquid crystalline medium. Science 278, 1111–1114 (1997).
Article PubMed ADS CAS Google Scholar
Chen, Y., Campbell, S. L. & Dokholyan, N. V. Deciphering protein dynamics from NMR data using explicit structure sampling and selection. Biophys. J. 93, 2300–2306 (2007).
Article PubMed PubMed Central ADS CAS Google Scholar
Tolman, J. R., Flanagan, J. M., Kennedy, M. A. & Prestegard, J. H. NMR evidence for slow collective motions in cyanometmyoglobin. Nat. Struct. Biol. 4, 292–297 (1997).
Article PubMed CAS Google Scholar
Swails, J., Zhu, T., He, X. & Case, D. A. AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules. J. Biomol. NMR 63, 125–139 (2015).
Article PubMed PubMed Central CAS Google Scholar
Schlagnitweit, J., Steiner, E., Karlsson, H. & Petzold, K. Efficient detection of structure and dynamics in unlabeled RNAs: the SELOPE approach. Chem. (Easton) 24, 6067–6070 (2018).
CAS Google Scholar
Liu, B., Rangadurai, A., Shi, H. & Al-Hashimi, H. M. Rapid assessment of Watson–Crick to Hoogsteen exchange in unlabeled DNA duplexes using high-power SELOPE imino 1 H CEST. Magn. Reson. 2, 715–731 (2021).
Article CAS Google Scholar
Hansen, M. R., Mueller, L. & Pardi, A. Tunable alignment of macromolecules by filamentous phage yields dipolar coupling interactions. Nat. Struct. Biol. 5, 1065–1074 (1998).
Article PubMed CAS Google Scholar
Zhang, Q., Stelzer, A. C., Fisher, C. K. & Al-Hashimi, H. M. Visualizing spatially correlated dynamics that directs RNA conformational transitions. Nature 450, 1263–1267 (2007).
Article PubMed ADS CAS Google Scholar
Salmon, L., Bascom, G., Andricioaei, I. & Al-Hashimi, H. M. A general method for constructing atomic-resolution RNA ensembles using NMR residual dipolar couplings: the basis for interhelical motions revealed. J. Am. Chem. Soc. 135, 5457–5466 (2013).
Article PubMed PubMed Central CAS Google Scholar
Al-Hashimi, H. M. et al. Concerted motions in HIV-1 TAR RNA may allow access to bound state conformations: RNA dynamics from NMR residual dipolar couplings. J. Mol. Biol. 315, 95–102 (2002).
Article PubMed CAS Google Scholar
Dethoff, E. A. et al. Characterizing complex dynamics in the transactivation response element apical loop and motional correlations with the bulge by NMR, molecular dynamics, and mutagenesis. Biophys. J. 95, 3906–3915 (2008).
Article PubMed PubMed Central ADS CAS Google Scholar
Zgarbová, M. et al. Refinement of the Cornell et al. Nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput. 7, 2886–2902 (2011).
Article PubMed PubMed Central Google Scholar
Hammond, N. B., Tolbert, B. S., Kierzek, R., Turner, D. H. & Kennedy, S. D. RNA Internal loops with tandem AG pairs: the structure of the 5′ G AG U/3′ U GA G loop can be dramatically different from others, including 5′ A AG U/3′ U GA A. Biochemistry 49, 5817–5827 (2010).
Article PubMed CAS Google Scholar
Nikolova, E. N., Gottardo, F. L. & Al-Hashimi, H. M. Probing transient hoogsteen hydrogen bonds in canonical duplex DNA using NMR relaxation dispersion and single-atom substitution. J. Am. Chem. Soc. 134, 3667–3670 (2012).
Article PubMed PubMed Central CAS Google Scholar
Zhao, B. & Zhang, Q. Measuring residual dipolar couplings in excited conformational states of nucleic acids by CEST NMR spectroscopy. J. Am. Chem. Soc. 137, 13480–13483 (2015).
Article PubMed PubMed Central CAS Google Scholar
Davis, A. R., Kirkpatrick, C. C. & Znosko, B. M. Structural characterization of naturally occurring RNA single mismatches. Nucleic Acids Res. 39, 1081–1094 (2011).
Article PubMed CAS Google Scholar
Heus, H. A., Wijmenga, S. S., Hoppe, H. & Hilbers, C. W. The detailed structure of tandem G·A mismatched base-pair motifs in RNA duplexes is context dependent. J. Mol. Biol. 271, 147–158 (1997).
Santa Lucia, J. Jr & Turner, D. H. Structure of (rGGCGAGCC)2 in solution from NMR and restrained molecular dynamics. Biochemistry 32, 12612–12623 (1993).
Article PubMed CAS Google Scholar
Pley, H. W., Flaherty, K. M. & McKay, D. B. Model for an RNA tertiary interaction from the structure of an intermolecular complex between a GAAA tetraloop and an RNA helix. Nature 372, 111–113 (1994).
Article PubMed ADS CAS Google Scholar
Huang, L., Ashraf, S., Wang, J. & Lilley, D. M. Control of box C/D snoRNP assembly by N6‐methylation of adenine. EMBO Rep. 18, 1631–1645 (2017).
Article PubMed PubMed Central CAS Google Scholar
Vögeli, B., Kazemi, S., Güntert, P. & Riek, R. Spatial elucidation of motion in proteins by ensemble-based structure calculation using exact NOEs. Nat. Struct. Mol. Biol. 19, 1053–1057 (2012).
Article PubMed Google Scholar
Anthis, N. J. & Clore, G. M. Visualizing transient dark states by NMR spectroscopy. Q Rev. Biophys. 48, 35–116 (2015).
Article PubMed PubMed Central CAS Google Scholar
Abou Assi, H. et al. 2′-O-Methylation can increase the abundance and lifetime of alternative RNA conformational states. Nucleic Acids Res. 48, 12365–12379 (2020).
Article PubMed PubMed Central Google Scholar
Kelly, M. L. et al. Understanding the characteristics of nonspecific binding of drug-like compounds to canonical stem-loop RNAs and their implications for functional cellular assays. RNA 27, 12–26 (2021).
Article PubMed PubMed Central CAS Google Scholar
Ganser, L. R. et al. High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble. Nat. Struct. Mol. Biol. 25, 425–434 (2018).
Article PubMed PubMed Central CAS Google Scholar
Delaglio, F. et al. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).
Article PubMed CAS Google Scholar
Ulrich, E. L. et al. BioMagResBank. Nucleic Acids Res. 36, D402–D408 (2007).
Article PubMed PubMed Central Google Scholar
Meissner, A. & Sørensen, O. W. The role of coherence transfer efficiency in design of TROSY-type multidimensional NMR experiments. J. Magn. Reson. 139, 439–442 (1999).
Kimsey, I. J., Petzold, K., Sathyamoorthy, B., Stein, Z. W. & Al-Hashimi, H. M. Visualizing transient Watson–Crick-like mispairs in DNA and RNA duplexes. Nature 519, 315–320 (2015).
Article PubMed PubMed Central ADS CAS Google Scholar
Zweckstetter, M. & Bax, A. Prediction of sterically induced alignment in a dilute liquid crystalline phase: aid to protein structure determination by NMR. J. Am. Chem. Soc. 122, 3791–3792 (2000).
Article CAS Google Scholar
Berendsen, H., Grigera, J. & Straatsma, T. The missing term in effective pair potentials. J. Phys. Chem. 91, 6269–6271 (1987).
Article CAS Google Scholar
Bashford, D. & Karplus, M. pKa’s of ionizable groups in proteins: atomic detail from a continuum electrostatic model. Biochemistry 29, 10219–10225 (1990).
Article PubMed CAS Google Scholar
Neese, F. Software update: the ORCA program system—Version 5.0. WIREs Comput. Mol. Sci. 12, e1606 (2022).
Article Google Scholar
Handy, N. C. & Cohen, A. J. Left-right correlation energy. Mol. Phys. 99, 403–412 (2001).
Article ADS CAS Google Scholar
Jensen, F. Segmented contracted basis sets optimized for nuclear magnetic shielding. J. Chem. Theory Comput. 11, 132–138 (2015).
Article PubMed CAS Google Scholar
Lu, X. J., Bussemaker, H. J. & Olson, W. K. DSSR: an integrated software tool for dissecting the spatial structure of RNA. Nucleic Acids Res. 43, e142 (2015).
PubMed PubMed Central Google Scholar
Roe, D. R. & Cheatham, T. E. III PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).
Article PubMed CAS Google Scholar
Coman, D. & Russu, I. M. A nuclear magnetic resonance investigation of the energetics of basepair opening pathways in DNA. Biophys. J. 89, 3285–3292 (2005).
Article PubMed PubMed Central ADS CAS Google Scholar

Download references

Acknowledgements

We thank Stephanie Gu and other members of the Al-Hashimi lab for their input. We acknowledge Duke Magnetic Resonance Spectroscopy Center and New York Structural Biology Center (NYSBC) for the technical support and resources. H.M.A. is a member of the New York Structural Biology Center (NYSBC). NMR experiments performed at NYSBC were funded by NIH grant S10 OD023499. This work was supported by US National Institute for General Medical Sciences (U54 AI150470 to H.M.A. and D.A.C and R01GM089846 to H.M.A.).

Author information

Laura Ganser
Present address: Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
Honglue Shi
Present address: Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA

Authors and Affiliations

Department of Biochemistry, Duke University School of Medicine, Durham, NC, 27710, USA
Ainan Geng & Laura Ganser
Center for Genomic and Computational Biology, Duke University School of Medicine, Durham, NC, 27710, USA
Rohit Roy
Department of Chemistry, Duke University, Durham, NC, 27708, USA
Honglue Shi
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, 10032, USA
Supriya Pratihar & Hashim M. Al-Hashimi
Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, 08854, USA
David A. Case

Authors

Ainan Geng
View author publications
You can also search for this author in PubMed Google Scholar
Laura Ganser
View author publications
You can also search for this author in PubMed Google Scholar
Rohit Roy
View author publications
You can also search for this author in PubMed Google Scholar
Honglue Shi
View author publications
You can also search for this author in PubMed Google Scholar
Supriya Pratihar
View author publications
You can also search for this author in PubMed Google Scholar
David A. Case
View author publications
You can also search for this author in PubMed Google Scholar
Hashim M. Al-Hashimi
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

A.G., L.G., and H.M.A. conceptualized the project and experimental design. A.G. with assistance from H.S. performed FARFAR calculations. A.G. performed sample and selection as well as other ensemble analysis. A.G. and L.G. prepared NMR samples. A.G., L.G. and S.P. performed NMR experiments. A.G. and H.M.A. with assistance from L.G. and R.R. analyzed the data. D.A.C. performed MD simulations and QM/MM chemical shift calculations. A.G. with assistance from R.R. prepared the figures. H.M.A. acquired funding and supervised the study. A.G. and H.M.A. wrote the manuscript with input from the remaining authors.

Corresponding author

Correspondence to Hashim M. Al-Hashimi.

Ethics declarations

Competing interests

H.M.A. is an adviser to and holds an ownership interest in Base4, an RNA-based drug discovery company. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review File

Description of Additional Supplementary Information

Supplementary Movie 1

Reporting Summary

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Geng, A., Ganser, L., Roy, R. et al. An RNA excited conformational state at atomic resolution. Nat Commun 14, 8432 (2023). https://doi.org/10.1038/s41467-023-43673-6

Download citation

Received: 25 May 2023
Accepted: 16 November 2023
Published: 19 December 2023
DOI: https://doi.org/10.1038/s41467-023-43673-6

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.