Abstract
Cellular processes are the product of interactions between biomolecules, which associate to form biologically active complexes1. 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 both on the strength of the contacts and 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 affect 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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All raw data represented in the manuscript figures can be accessed publicly on Figshare at https://doi.org/10.6084/m9.figshare.21210770. Further information is available from the corresponding authors upon request.
Code availability
Code written to analyse FARFAR-NMR ensembles can be found on GitHub at https://github.com/alhashimilab/TAR_Thermodynamic_Model/.
Change history
09 June 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06286-z
References
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).
Fischer, M., Coleman, R. G., Fraser, J. S. & Shoichet, B. K. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat. Chem. 6, 575–583 (2014).
Parvin, J. D., Mc Cormick, R. J., Sharp, P. A. & Fisher, D. E. Pre-bending of a promoter sequence enhances affinity for the TATA-binding factor. Nature 373, 724–727 (1995).
Denny, S. K. et al. High-throughput investigation of diverse junction elements in RNA tertiary folding. Cell 174, 377–390.e20 (2018).
Afek, A. et al. DNA mismatches reveal conformational penalties in protein–DNA recognition. Nature 587, 291–296 (2020).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Orlovsky, N. I., Al-Hashimi, H. M. & Oas, T. G. Exposing hidden high-affinity RNA conformational states. J. Am. Chem. Soc. 142, 907–921 (2020).
Alderson, T. R. & Kay, L. E. NMR spectroscopy captures the essential role of dynamics in regulating biomolecular function. Cell 184, 577–595 (2021).
Guo, J. U. & Bartel, D. P. RNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria. Science 353, aaf5371 (2016).
Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J. S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).
Shi, H. et al. Rapid and accurate determination of atomistic RNA dynamic ensemble models using NMR and structure prediction. Nat. Commun. 11, 5531 (2020).
Tomezsko, P., Swaminathan, H. & Rouskin, S. Viral RNA structure analysis using DMS-MaPseq. Methods 183, 68–75 (2020).
Schulze-Gahmen, U. & Hurley, J. H. Structural mechanism for HIV-1 TAR loop recognition by Tat and the super elongation complex. Proc. Natl Acad. Sci. USA 115, 12973–12978 (2018).
D’Orso, I. et al. Transition step during assembly of HIV Tat:P-TEFb transcription complexes and transfer to TAR RNA. Mol. Cell. Biol. 32, 4780 (2012).
Liu, Y., Suñé, C. & Garcia-Blanco, M. A. Human immunodeficiency virus type 1 Tat-dependent activation of an arrested RNA polymerase II elongation complex. Virology 255, 337–346 (1999).
Marciniak, R. A., Calnan, B. J., Frankel, A. D. & Sharp, P. A. HIV-1 Tat protein trans-activates transcription in vitro. Cell 63, 791–802 (1990).
Karn, J., Stoltzfus, C. M., Bushman, F. D., Nabel, G. J. & Swanstrom, R. Transcriptional and posttranscriptional regulation of HIV-1 gene expression. Cold Spring Harbor Perspect. Med. 2, a006916 (2012).
Merriman, D. K. et al. Increasing the length of poly-pyrimidine bulges broadens RNA conformational ensembles with minimal impact on stacking energetics. RNA 24, 1363–1376 (2018).
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).
Puglisi, J. D., Tan, R., Calnan, B. J., Frankel, A. D. & Williamson, J. R. Conformation of the TAR RNA–arginine complex by NMR spectroscopy. Science 257, 76–80 (1992).
Tao, J., Chen, L. & Frankel, A. D. Dissection of the proposed base triple in human immunodeficiency virus TAR RNA indicates the importance of the Hoogsteen interaction. Biochemistry 36, 3491–3495 (1997).
Pham, V. V. et al. HIV-1 Tat interactions with cellular 7SK and viral TAR RNAs identifies dual structural mimicry. Nat. Commun. 9, 4266 (2018).
Brodsky, A. S. & Williamson, J. R. Solution structure of the HIV-2 TAR-argininamide complex. J. Mol. Biol. 267, 624–639 (1997).
Calnan, B. J., Tidor, B., Biancalana, S., Hudson, D. & Frankel, A. D. Arginine-mediated RNA recognition: the arginine fork. Science 252, 1167–1171 (1991).
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).
Chavali, S. S., Cavender, C. E., Mathews, D. H. & Wedekind, J. E. Arginine forks are a widespread motif to recognize phosphate backbones and guanine nucleobases in the RNA major groove. J. Am. Chem. Soc. 142, 19835–19839 (2020).
Mustoe, A. M., Al-Hashimi, H. M. & Brooks, C. L. Coarse grained models reveal essential contributions of topological constraints to the conformational free energy of RNA bulges. J. Phys. Chem. B 118, 40 (2014).
Karn, J., Dingwall, C., Finch, J. T., Heaphy, S. & Gait, M. J. RNA binding by the tat and rev proteins of HIV-1. Biochimie 73, 9–16 (1991).
Barthel, A. & Zacharias, M. Conformational transitions in RNA single uridine and adenosine bulge structures: a molecular dynamics free energy simulation study. Biophys. J. 90, 2450–2462 (2006).
Qi, Y. et al. Continuous interdomain orientation distributions reveal components of binding thermodynamics. J. Mol. Biol. 430, 3412–3426 (2018).
Watkins, A. M., Rangan, R. & Das, R. FARFAR2: improved de novo Rosetta prediction of complex global RNA folds. Structure 28, 963–976.e6 (2020).
Matsumoto, C., Hamasaki, K., Mihara, H. & Ueno, A. A high-throughput screening utilization intramolecular fluorescence resonance energy transfer for the discovery of the molecules that bind HIV-1 TAR RNA specifically. Bioorganic Med. Chem. Lett. 10, 1857–1861 (2000).
Ganser, L. R. et al. Probing RNA conformational equilibria within the functional cellular context. Cell Rep. 30, 2472–2480.e4 (2020).
Taliaferro, J. M. et al. RNA sequence context effects measured in vitro predict in vivo protein binding and regulation. Mol. Cell 64, 294–306 (2016).
Delaglio, F. et al. NMRPipe: A multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).
Goddard, T. D. & Kneller, D. G. Sparky—NMR Assignment and Integration Software https://www.cgl.ucsf.edu/home/sparky/ (Univ. California, San Francisco, 2006).
Casiano-Negroni, A., Sun, X. & Al-Hashimi, H. M. Probing Na+-induced changes in the HIV-1 TAR conformational dynamics using NMR residual dipolar couplings: New insights into the role of counterions and electrostatic interactions in adaptive recognition. Biochemistry 46, 6525–6535 (2007).
Jarmoskaite, I., AlSadhan, I., Vaidyanathan, P. P. & Herschlag, D. How to measure and evaluate binding affinities. eLife 9, e57254 (2020).
Tiley, L. S., Madore, S. J., Malim, M. H. & Cullen, B. R. The VP16 transcription activation domain is functional when targeted to a promoter-proximal RNA sequence. Genes Dev. 6, 2077–2087 (1992).
Schulze-Gahmen, U. et al. The AFF4 scaffold binds human P-TEFb adjacent to HIV Tat. eLife 2, e00327 (2013).
Shojania, S. & O’Neil, J. D. HIV-1 Tat is a natively unfolded protein: the solution conformation and dynamics of reduced HIV-1 Tat-(1–72) by NMR spectroscopy. J. Biol. Chem. 281, 8347–8356 (2006).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671 (2012).
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).
Sathyamoorthy, B., Lee, J., Kimsey, I., Ganser, L. R. & Al-Hashimi, H. Development and application of aromatic [13C, 1H] SOFAST-HMQC NMR experiment for nucleic acids. J. Biomol. NMR 60, 77–83 (2014).
Acknowledgements
The authors thank the Duke Magnetic Resonance Spectroscopy Center for nuclear magnetic resonance resources and J. Shin for assistance with statistical analysis. This work was supported by the National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) grant R01GM132899 to H.M.A.-H. and D.H., as well as the NIH National Institutes of Allergy and Infectious Disease (NIAID) grants U54 AI150470 to H.M.A.-H., F30 AI143282- 01A1 to M.L.K., and 5R21AI156915 to U.S.-G. U.S.-G. was also supported by a grant from NIH/NIAID, UM1AI164559, HIV Obstruction by Programmed Epigenetics, co-funded by NHLBI, NIDA, NIMH, NINDS and NIDDK.
Author information
Authors and Affiliations
Contributions
M.L.K, H.M.A.-H. and D.H. conceptualized the study. M.L.K., R.R., A.G., L.R.G. and U.S.-G. designed the experiments and collected the data. M.L.K performed the NMR experiments and analysed the data. R.R. performed FARFAR-NMR analysis. M.L.K., A.G. and R.R. performed in vitro Tat-ARM peptide binding experiments, and M.L.K. analysed the data. M.L.K., A.G. and L.R.G. performed the cellular transactivation experiments, and M.L.K. analysed the data. M.L.K., R.R. and A.M. conceptualized the methodology for calculating free energy of cellular transactivation. U.S.-G. performed the in vitro TAR–Tat–SEC binding assays and analysed the data. M.L.K. created the figures. H.M.A.-H. and D.H. acquired funding. H.M.A.-H., D.H. and B.R.C. supervised the study. M.L.K., H.M.A. and D.H. wrote the manuscript with input from the other authors.
Corresponding authors
Ethics declarations
Competing interests
H.M.A.-H. is an adviser to and holds an ownership interest in Base4, an RNA-based drug discovery company. D.H. is a consultant for Radial, an RNA-based drug discovery company.
Peer review
Peer review information
Nature thanks Beat Vogeli, Joseph Wedekind and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Measurement of pstack by 2D aromatic [13C, 1H] SOFAST-HMQC44.
a, pstack (see Methods) for all TAR mutants U0-7 and wt with and without the A27U-U38A and A27-deaza-N7 base-triple disrupting mutations. b, Differences in ΔΔGpenalty,stack between the wt A27-U38 version of each bulge variant to its two corresponding base-triple disrupted variants, U27-A38 and N7-deaza-A27, are small (within +/−0.3 kcal/mol). This correspondence in stacking is indicated by the strong linear correlations observed between stacked populations for the wt base triple mutants versus their base triple disrupted counterparts, Pearson correlation (r) and line of best fit shown, where the colors correspond to the bulge length as shown in panel a. c, Sets of overlayed spectra for wt and all bulge mutants U0-7. For each, the wt base triple construct is black and the base-triple disrupting mutants are overlayed, A27U-U38A in blue and A27-deaza-N7 in green. The wt spectrum is fully assigned, for the bulge constructs the stacking reporter residues A22 and U23 are indicated.
Extended Data Fig. 2 NMR evidence for U-U wobbles in the U7 TAR variant.
The 1H 1D imino NMR spectrum of the U7 variant shows resonances in the 10-12 ppm region, suggesting the U-rich bulge might transiently form a short helix comprised of U-U wobble mismatches which could in turn promote stacking of the TAR helices.
Extended Data Fig. 3 TAR-Tat-ARM peptide binding assay.
a, Binding curves for individual TAR variants, with all five independent experiments overlayed (black: experiment 1, red: experiment 2, orange: experiment 3, yellow: experiment 4, green: experiment 5). The data points for each individual curve represent the mean fluorescence values, and the error bars represent the standard deviation, of 3 technical replicates. Each individual curve was fit to equation 1, and average Kd values +/− the standard deviation over the five independent experiments are displayed for each mutant. b, One experiment (experiment 5) of representative fluorescence binding curves for all TAR mutants overlayed. The data points for each individual curve represent the mean fluorescence values, and the error bars represent the standard deviations, of 3 technical replicates. c, Observed dissociation constants do not change as the concentration of the constant component (Tat-ARM peptide) is varied, as expected for accurate Kd measurements38. Dissociation constants were measured for wt and U2 at multiple concentrations of Tat-ARM peptide, varying 50-fold. The dissociation constants for wt and U2 remain vary < 2-fold over this range. The data points for each individual curve represent the mean fluorescence values, and the error bars represent the standard deviation, of 3 technical replicates. Each individual curve was fit to equation 1, and average Kd values +/− the standard deviation over the 1 (wt-2 nM, wt-100 nM), 2 (U2-2 nM, U2-20 nM, U2-100 nM), or 3 (wt-20 nM) independent experiments are displayed for each mutant. d, Observed dissociation constants do not change as the equilibration time is varied, as expected for accurate Kd measurements38. Shown are Kd measurements for wt at varying timepoints to demonstrate the reaction has reached equilibrium. The Kd value does not decrease with increasing incubation times, indicating the reaction has reached equilibrium at the lowest timepoint. The same assay plate was read at each time point, creating a photobleaching effect at each subsequent timepoint, which is evident in the increasing baseline values. The data points for each individual curve represent the mean fluorescence values, and the error bars represent the standard deviation, of 3 technical replicates. Each individual curve was fit to equation 1, with the resulting Kd values displayed.
Extended Data Fig. 4 Stacking and peptide binding energetics for wt and U2-7.
ΔΔGpep versus ΔΔGpenalty,stack for base-triple destabilized mutants, A27U-U38A (left) and A27-deaza-N7 (right), correlates poorly (Pearson correlation shown). Grey lines indicate the best fit (equation shown), and black lines indicate slope of 1, which is the prediction of the model in the absence of the base triple disrupting mutations. Error bars represent the standard deviation of 5 independent experiments measuring ΔΔGpep.
Extended Data Fig. 5 Energetics of base-triple disruption in Tat-ARM binding and cellular transactivation.
a, Changes in fluorescence upon peptide binding is greater for base-triple competent variants than for base-triple disrupted variants. Shown are the fitted minimum and maximum fluorescence values (from equation 1, see Methods) from the TAR-Tat-ARM peptide binding assay for 5 independent experiments. Red dotted lines indicate average maximum values for the base-triple competent variants (190), and base-triple disrupted variants (155). U0-1 are shown in grey as they are unable to form the base-triple. b, Energy diagram of Tat-ARM peptide binding to base triple competent and base-triple disrupted variants. The peptide can bind a bulge-independent kinked TAR conformation lower in energy than the base-triple disrupted stacked conformation. c, Energy diagram of Tat:SEC binding to TAR in the cellular context. The favorable interactions between Cyclin T1 and the TAR apical loop are unable to form in the kinked state of TAR, and so each base-triple disrupted variant is destabilized by the same amount (ctriple) and binds its non-base triple stacked state (demarcated with an asterisk*). d, Proposed model for an alternative sheared base-triple conformation in the A27U-U38A base-triple disrupting mutants with hydrogen bonds shown as black dashed line (left). Two views of the 3D structural model for the alternative sheared base-triple conformation obtained by replacing A27 with U and U38 with A in the PDBID:6MCE22 U2 TAR structure (right).
Extended Data Fig. 6 Cellular transactivation assay.
a, Representative example of luminescence data for one biological replicate of U0-7 and wt (3 technical replicates). Shown are luminescence values for Firefly luciferase, reporting on transactivation (top), luminescence values for Renilla luciferase under control of a CMV promoter, used as a control for transfection (middle), and the ratio FLuc/RLuc to normalize for differences arising from transient transfection (bottom), with the error bars representing the standard deviations of those values over 3 technical replicates. b, Aggregate FLuc/RLuc data for all TAR mutants over 5 independent experiments (biological replicates). Mutants labelled with (*) indicate the A27U/U38A base-triple disrupting mutation. In all graphs, red data are values when Tat is co-transfected and black data are values without Tat, representing Tat-independent baseline activity. Error bars represent the standard deviation in FLuc/RLuc values over 5 biological replicates. c, Model of Tat-dependent versus Tat-independent transactivation energetics in cells. (Top) The observed level of basal transcription is likely due to many nonspecific binding interactions of the preformed SEC complex to TAR, which does not alter the conformational propensities of the TAR ensemble and has a low probability of achieving an active bound conformation leading to transactivation and transcription. (Bottom) In Tat-dependent transactivation, the presence of Tat increases the binding affinity to form the active bound state, leading to higher levels of transactivation and transcription. d, Tat plasmid titration. In this experiment, the concentration of Tat was varied for wt (black), one of the most transactivating constructs, and U0 (red), one of the least transactivating constructs. We see that for both wt and U0, the level of transactivation (FLuc/RLuc) increases with an increase in Tat, indicating that the reaction is not saturated at the level of Tat we are using (20 ng). Dots are the individual FLuc/RLuc values and error bars represent the standard deviation in these values over 3 independent experiments. e, Larger scale Tat plasmid titrations for wt and U0 covering four orders of magnitude, with the y-axis being FLuc signal normalized to the average FLuc value measured for wt at 20 ng Tat. Again, for both mutants, the level of transactivation continually increases with an increase of Tat plasmid; the value we use in our assays (20 ng, red dot) is at the low end of this spectrum. Dots represent the average, and error bars the standard deviation, of normalized FLuc luminescence values over 3 independent experiments.
Extended Data Fig. 7 Measurements of TAR-Tat:SEC binding using electrophoretic mobility shift assay (EMSA).
Shown are EMSA binding curves for TAR bulge mutants U0,1,2,4,6,7 and UCU along with average apparent Kd values (see Methods) for each variant, obtained by fitting data to equation 2 using GraphPad Prism (version 9.3.1). Binding curves from 2 (U0, U2, U6, U7) or 3 (wt, U1, U4) independent experiments are overlayed (black: experiment 1, red: experiment 2, orange: experiment 3). Below the binding curves for each variant is one representative EMSA gel (experiment 1) of 2 total gels (U0, U2, U6, U7) or 3 total gels (wt, U1, U4) for each variant.
Extended Data Fig. 8 Model of steric interaction between the U7 bulge and P-TEFb.
(Left) FARFAR models of representative base-triple conformations of wt and U7 bound to the Tat:SEC complex. (Right) Zoomed in view of the bulge interaction with P-TEFb. In dashed red lines are atom distances between bulge residues and P-TEFb that are within 2.5 Å, representing steric overlap. U7 (bottom) has multiple steric overlaps, whereas wt (top) does not.
Supplementary information
Supplementary Information
This file contains the full Methods section for the manuscript, Methods references, supplementary Discussions 1 and 2, Supplementary Tables 1–6 and corresponding legends, and the legend for Supplementary Table 7.
Supplementary Table 7
This file contains all sequences for RNAs and oligonucleotides used in this study.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ken, M.L., Roy, R., Geng, A. et al. RNA conformational propensities determine cellular activity. Nature 617, 835–841 (2023). https://doi.org/10.1038/s41586-023-06080-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-06080-x
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.
