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High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

Nature Structural & Molecular Biology volume 25, pages 425–434 (2018)Cite this article

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A Publisher Correction to this article was published on 06 May 2020

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Abstract

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sublibraries optimized for evaluating enrichment when virtually screening a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics simulations. Ensemble-based virtual screening scores molecules with an area under the receiver operator characteristic curve of ~0.85–0.94 and with ~40–75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same molecular dynamics simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits and that the degree of enrichment is dependent on the accuracy of the ensemble.

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Fig. 1: Experimental HTS of HIV-1 TAR RNA to generate libraries for EBVS.
Fig. 2: Evaluating EBVS against the RDC TAR dynamic ensemble.
Fig. 3: Dependence of EBVS enrichment on ensemble size and ensemble accuracy.
Fig. 4: Assessing EBVS-predicted small-molecule bound TAR conformations.
Fig. 5: Evaluating ligand-bound poses predicted using EBVS.

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  • 06 May 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Howe, J. A. et al. Selective small-molecule inhibition of an RNA structural element. Nature 526, 672–677 (2015).

    CAS  PubMed  Google Scholar 

  2. Connelly, C. M., Moon, M. H. & Schneekloth, J. S. Jr. The emerging role of RNA as a therapeutic target for small molecules. Cell Chem. Biol. 23, 1077–1090 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Shortridge, M. D. & Varani, G. Structure based approaches for targeting non-coding RNAs with small molecules. Curr. Opin. Struct. Biol. 30, 79–88 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Hermann, T. Small molecules targeting viral RNA. Wiley Interdiscip. Rev. RNA 7, 726–743 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Matsui, M. & Corey, D. R. Non-coding RNAs as drug targets. Nat. Rev. Drug Discov. 16, 167–179 (2017).

    CAS  PubMed  Google Scholar 

  6. Disney, M. D., Yildirim, I. & Childs-Disney, J. L. Methods to enable the design of bioactive small molecules targeting RNA. Org. Biomol. Chem. 12, 1029–1039 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Feixas, F., Lindert, S., Sinko, W. & McCammon, J. A. Exploring the role of receptor flexibility in structure-based drug discovery. Biophys. Chem. 186, 31–45 (2014).

    CAS  PubMed  Google Scholar 

  8. Amaro, R. E. & Li, W. W. Emerging methods for ensemble-based virtual screening. Curr. Top. Med. Chem. 10, 3–13 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Stelzer, A. C. et al. Discovery of selective bioactive small molecules by targeting an RNA dynamic ensemble. Nat. Chem. Biol. 7, 553–559 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Ferrari, A. M., Wei, B. Q., Costantino, L. & Shoichet, B. K. Soft docking and multiple receptor conformations in virtual screening. J. Med. Chem. 47, 5076–5084 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Cerqueira, N. M. F. S. A., Bras, N. F., Fernandes, P. A. & Ramos, M. J. MADAMM: a multistaged docking with an automated molecular modeling protocol. Proteins 74, 192–206 (2009).

    CAS  PubMed  Google Scholar 

  12. Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A. & Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 49, 534–553 (2006).

    CAS  PubMed  Google Scholar 

  13. Knegtel, R. M. A., Kuntz, I. D. & Oshiro, C. M. Molecular docking to ensembles of protein structures. J. Mol. Biol. 266, 424–440 (1997).

    CAS  PubMed  Google Scholar 

  14. Carlson, H. A. et al. Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem. 43, 2100–2114 (2000).

    CAS  PubMed  Google Scholar 

  15. Lin, J.-H., Perryman, A. L., Schames, J. R. & McCammon, J. A. Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J. Am. Chem. Soc. 124, 5632–5633 (2002).

    CAS  PubMed  Google Scholar 

  16. Yang, S., Salmon, L. & Al-Hashimi, H. M. Measuring similarity between dynamic ensembles of biomolecules. Nat. Methods 11, 552–554 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Salmon, L. et al. Modulating RNA alignment using directional dynamic kinks: application in determining an atomic-resolution ensemble for a hairpin using NMR residual dipolar couplings. J. Am. Chem. Soc. 137, 12954–12965 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Barril, X. & Morley, S. D. Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J. Med. Chem. 48, 4432–4443 (2005).

    CAS  PubMed  Google Scholar 

  19. Craig, I. R., Essex, J. W. & Spiegel, K. Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J. Chem. Inf. Model. 50, 511–524 (2010).

    CAS  PubMed  Google Scholar 

  20. Tian, S. et al. Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. J. Chem. Inf. Model. 54, 2664–2679 (2014).

    CAS  PubMed  Google Scholar 

  21. Treiber, D. K. & Williamson, J. R. Beyond kinetic traps in RNA folding. Curr. Opin. Struct. Biol. 11, 309–314 (2001).

    CAS  PubMed  Google Scholar 

  22. Blackledge, M. Recent progress in the study of biomolecular structure and dynamics in solution from residual dipolar couplings. Prog. Nucl. Magn. Reson. Spectrosc. 46, 23–61 (2005).

    CAS  Google Scholar 

  23. Frank, A. T., Stelzer, A. C., Al-Hashimi, H. M. & Andricioaei, I. Constructing RNA dynamical ensembles by combining MD and motionally decoupled NMR RDCs: new insights into RNA dynamics and adaptive ligand recognition. Nucleic Acids Res. 37, 3670–3679 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Lange, O. F. et al. Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320, 1471–1475 (2008).

    CAS  PubMed  Google Scholar 

  26. Salmon, L., Yang, S. & Al-Hashimi, H. M. Advances in the determination of nucleic acid conformational ensembles. Annu. Rev. Phys. Chem. 65, 293–316 (2014).

    CAS  PubMed  Google Scholar 

  27. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Tóth, G. et al. Targeting the intrinsically disordered structural ensemble of α-synuclein by small molecules as a potential therapeutic strategy for Parkinson’s disease. PLoS One 9, e87133 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. 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).

    CAS  PubMed  Google Scholar 

  30. 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).

    CAS  PubMed  Google Scholar 

  31. Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582–6594 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen, L., Calin, G. A. & Zhang, S. Novel insights of structure-based modeling for RNA-targeted drug discovery. J. Chem. Inf. Model. 52, 2741–2753 (2012).

    CAS  PubMed  Google Scholar 

  33. Li, Y. et al. Accuracy assessment of protein-based docking programs against RNA targets. J. Chem. Inf. Model. 50, 1134–1146 (2010).

    CAS  PubMed  Google Scholar 

  34. Morley, S. D. & Afshar, M. Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock. J. Comput. Aided Mol. Des. 18, 189–208 (2004).

    CAS  PubMed  Google Scholar 

  35. Aboul-ela, F. Strategies for the design of RNA-binding small molecules. Future Med. Chem. 2, 93–119 (2010).

    CAS  PubMed  Google Scholar 

  36. Verdonk, M. L. et al. Virtual screening using protein-ligand docking: avoiding artificial enrichment. J. Chem. Inf. Comput. Sci. 44, 793–806 (2004).

    CAS  PubMed  Google Scholar 

  37. Abagyan, R., Totrov, M. & Kuznetsov, D. ICM - a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994).

    CAS  Google Scholar 

  38. Neves, M. A. C., Totrov, M. & Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des. 26, 675–686 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Brozell, S. R. et al. Evaluation of DOCK 6 as a pose generation and database enrichment tool. J. Comput. Aided Mol. Des. 26, 749–773 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Gaudreault, F. & Najmanovich, R. J. FlexAID: revisiting docking on non-native-complex structures. J. Chem. Inf. Model. 55, 1323–1336 (2015).

    CAS  PubMed  Google Scholar 

  41. Aboul-ela, F., Karn, J. & Varani, G. Structure of HIV-1 TAR RNA in the absence of ligands reveals a novel conformation of the trinucleotide bulge. Nucleic Acids Res. 24, 3974–3981 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Shi, H. & Moore, P. B. The crystal structure of yeast phenylalanine tRNA at 1.93 A resolution: a classic structure revisited. RNA 6, 1091–1105 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Yang, S. & Al-Hashimi, H. M. Unveiling inherent degeneracies in determining population- weighted ensembles of interdomain orientational distributions using NMR residual dipolar couplings: application to RNA helix junction helix motifs. J. Phys. Chem. B 119, 9614–9626 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Du, Z., Lind, K. E. & James, T. L. Structure of TAR RNA complexed with a Tat-TAR interaction nanomolar inhibitor that was identified by computational screening. Chem. Biol. 9, 707–712 (2002).

    CAS  PubMed  Google Scholar 

  45. Murchie, A. I. H. et al. Structure-based drug design targeting an inactive RNA conformation: exploiting the flexibility of HIV-1 TAR RNA. J. Mol. Biol. 336, 625–638 (2004).

    CAS  PubMed  Google Scholar 

  46. Davis, B. et al. Rational design of inhibitors of HIV-1 TAR RNA through the stabilisation of electrostatic “hot spots”. J. Mol. Biol. 336, 343–356 (2004).

    CAS  PubMed  Google Scholar 

  47. Faber, C., Sticht, H., Schweimer, K. & Rösch, P. Structural rearrangements of HIV-1 Tat-responsive RNA upon binding of neomycin B. J. Biol. Chem. 275, 20660–20666 (2000).

    CAS  PubMed  Google Scholar 

  48. Aboul-ela, F., Karn, J. & Varani, G. The structure of the human immunodeficiency virus type-1 TAR RNA reveals principles of RNA recognition by Tat protein. J. Mol. Biol. 253, 313–332 (1995).

    CAS  PubMed  Google Scholar 

  49. Bailor, M. H., Sun, X. & Al-Hashimi, H. M. Topology links RNA secondary structure with global conformation, dynamics, and adaptation. Science 327, 202–206 (2010).

    CAS  PubMed  Google Scholar 

  50. Pitt, S. W., Majumdar, A., Serganov, A., Patel, D. J. & Al-Hashimi, H. M. Argininamide binding arrests global motions in HIV-1 TAR RNA: comparison with Mg2+-induced conformational stabilization. J. Mol. Biol. 338, 7–16 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Pitt, S. W., Zhang, Q., Patel, D. J. & Al-Hashimi, H. M. Evidence that electrostatic interactions dictate the ligand-induced arrest of RNA global flexibility. Angew. Chem. Int. Edn. Engl. 44, 3412–3415 (2005).

    CAS  Google Scholar 

  52. Lang, P. T. et al. DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15, 1219–1230 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Rueda, M., Bottegoni, G. & Abagyan, R. Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50, 186–193 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Nichols, S. E., Baron, R., Ivetac, A. & McCammon, J. A. Predictive power of molecular dynamics receptor structures in virtual screening. J. Chem. Inf. Model. 51, 1439–1446 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Korb, O. et al. Potential and limitations of ensemble docking. J. Chem. Inf. Model. 52, 1262–1274 (2012).

    CAS  PubMed  Google Scholar 

  56. Lind, K. E., Du, Z., Fujinaga, K., Peterlin, B. M. & James, T. L. Structure-based computational database screening, in vitro assay, and NMR assessment of compounds that target TAR RNA. Chem. Biol. 9, 185–193 (2002).

    CAS  PubMed  Google Scholar 

  57. Yoon, S. & Welsh, W. J. Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring. J. Chem. Inf. Comput. Sci. 44, 88–96 (2004).

    CAS  PubMed  Google Scholar 

  58. 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).

    CAS  PubMed  Google Scholar 

  59. Bailor, M. H., Mustoe, A. M., Brooks, C. L. III & Al-Hashimi, H. M. 3D maps of RNA interhelical junctions. Nat. Protoc. 6, 1536–1545 (2011).

    CAS  PubMed  Google Scholar 

  60. Matsumoto, C., Hamasaki, K., Mihara, H. & Ueno, A. A high-throughput screening utilizing intramolecular fluorescence resonance energy transfer for the discovery of the molecules that bind HIV-1 TAR RNA specifically. Bioorg. Med. Chem. Lett. 10, 1857–1861 (2000).

    CAS  PubMed  Google Scholar 

  61. Calnan, B. J., Biancalana, S., Hudson, D. & Frankel, A. D. Analysis of arginine-rich peptides from the HIV Tat protein reveals unusual features of RNA-protein recognition. Genes Dev. 5, 201–210 (1991).

    CAS  PubMed  Google Scholar 

  62. Loret, E. P., Georgel, P., Johnson, W. C. Jr. & Ho, P. S. Circular dichroism and molecular modeling yield a structure for the complex of human immunodeficiency virus type 1 trans-activation response RNA and the binding region of Tat, the trans-acting transcriptional activator. Proc. Natl Acad. Sci. USA 89, 9734–9738 (1992).

    CAS  PubMed  Google Scholar 

  63. 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).

    CAS  PubMed  Google Scholar 

  64. Zhang, J.-H., Chung, T. D. Y. & Oldenburg, K. R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 67–73 (1999).

    CAS  PubMed  Google Scholar 

  65. Sathyamoorthy, B., Lee, J., Kimsey, I., Ganser, L. R. & Al-Hashimi, H. Development and application of aromatic [(13)C, (1)H] SOFAST-HMQC NMR experiment for nucleic acids. J. Biomol. NMR 60, 77–83 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Delaglio, F. et al. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).

    CAS  Google Scholar 

  67. Goddard, T. D. & Kneller, D. G. SPARKY 3 https://www.cgl.ucsf.edu/home/sparky/ (University of California, San Francisco, 2008).

  68. Sutter, P. & Weis, C. D. Ring opening reactions of 6H-anthra[1,9-cd]isoxazol-6-ones and related compounds. J. Heterocycl. Chem. 19, 997–1011 (1982).

    CAS  Google Scholar 

  69. Walter, F., Vicens, Q. & Westhof, E. Aminoglycoside-RNA interactions. Curr. Opin. Chem. Biol. 3, 694–704 (1999).

    CAS  PubMed  Google Scholar 

  70. 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).

    CAS  Google Scholar 

  71. Zweckstetter, M. NMR: prediction of molecular alignment from structure using the PALES software. Nat. Protoc. 3, 679–690 (2008).

    CAS  PubMed  Google Scholar 

  72. Hansen, A. L. & Al-Hashimi, H. M. Insight into the CSA tensors of nucleobase carbons in RNA polynucleotides from solution measurements of residual CSA: towards new long-range orientational constraints. J. Magn. Reson. 179, 299–307 (2006).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank M. Larsen and S. Vander Roest (University of Michigan Center of Chemical Genomics) for their help and input in carrying out high-throughput screening. We also thank the Duke Magnetic Resonance Spectroscopy Center for NMR resources and assistance in carrying out experiments and thank the Duke Compute Cluster for computational resources and support. This work was supported by the US National Institutes of Health (P50 GM103297, R01 AI066975 and P01 GM0066275 to H.M.A.-H.; T32 GM08487 and F31 GM119306 to L.R.G.).

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Authors and Affiliations

  1. Department of Biochemistry, Duke University School of Medicine, Durham, NC, USA

    Laura R. Ganser, Atul Rangadurai, Megan L. Kelly, Aman D. Kansal, Bharathwaj Sathyamoorthy & Hashim M. Al-Hashimi

  2. Department of Chemistry, University of Michigan, Ann Arbor, MI, USA

    Janghyun Lee

  3. Department of Chemistry, Duke University, Durham, NC, USA

    Dawn K. Merriman & Hashim M. Al-Hashimi

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Contributions

Experiments were designed by L.R.G., J.L., and H.M.A.-H.; performed by L.R.G., J.L., A.R., D.K.M., M.L.K., A.D.K., and B.S.; and analyzed by L.R.G. and H.M.A.-H. L.R.G. and H.M.A.-H. wrote the manuscript with input from all other authors.

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Correspondence to Hashim M. Al-Hashimi.

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Competing interests

H.M.A.-H. is an advisor to and holds an ownership interest in Nymirum Inc., an RNA-based drug discovery company. Some of the technology used in this paper has been licensed to Nymirum.

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Integrated supplementary information

Supplementary Figure 1 HTS identified seven hit molecules that bind TAR and displace Tat peptide.

a. Tat-displacement assays for the hit molecules in the absence (black) and presence (gray) of 100-fold excess tRNA. Points represent the mean and error bars represent the s.d. from n = 3 independent experiments. b. Overlay of SOFAST-[1H-13C] HMQC NMR spectra of 50 μM TAR free (black) and in the presence of 3X hit molecule (red, purple or blue). Spectra are overlaid for hits that induced similar chemical shift perturbations. (*) denotes folded peaks.

Supplementary Figure 2 NMR identified false positive molecules from HTS.

a. Examples of molecules from HTS that show activity in the Tat-displacement assay, but that do not bind TAR by NMR. b. Tat-displacement assay (black) and Tat-only control assay (gray). Points represent the mean and error bars represent the s.d. from n = 3 independent experiments. c. Overlay of SOFAST-[1H-13C] HMQC NMR spectra of 50 μM TAR both free (black) and in the presence of 6X CCG-111926 (red), 4X CCG-106134 (red), or 3X CCG-160257 (red). (*) denotes folded peaks.

Supplementary Figure 3 Testing for false positives in HTS by assaying compounds that are chemically similar to hit molecules.

a. Examples of molecules with chemical similarity to hit molecules that do not bind TAR. b. Tat-displacement assays (points saturating the fluorescence reader are removed). Points represent the mean and error bars represent the s.d. from n = 3 independent experiments. c. Overlay of SOFAST-[1H-13C] HMQC NMR spectra of 50 μM TAR both free (black) and in the presence of 3X small molecule (red). (*) denotes folded peaks.

Supplementary Figure 4 Testing for false positives in HTS based on EBVS scores.

a. TAR-binding molecules identified by testing a set of ten molecules from the top 5% of docking scores b. Tat-displacement assays in the absence (black) and presence (gray) of 100-fold excess tRNA (points saturating the fluorescence reader are removed). Points represent the mean and error bars represent the s.d. from n = 3 independent experiments. c. Overlay of SOFAST-[1H-13C] HMQC NMR spectra of 50 μM TAR both free (black) and in the presence of 3X molecule (red). (*) denotes folded peaks.

Supplementary Figure 5 Hit molecules of the Filtered and Optimized libraries identified in the literature.

Hit number corresponds to Supplementary Table 1 and (*) denotes molecules also in the Optimized library.

Supplementary Figure 6 ROC plot AUC values are robust across varied methods of defining hits and non-hits.

Variations in the similarity cutoff and number of non-hits selected per hit results in minor changes in the a. chemical property distributions and b. ROC plots for the Optimized library. c. ROC plots for all libraries (Full, Filtered and Optimized libraries) when using the Boltzmann-weighted average score, arithmetic average score, and best score for all hits before (purple) and after (blue) clustering by Bemis-Murcko atomic framework as well as cell-active hits before (red) and after (orange) clustering. ROC plots were generated from one run of docking all molecules to all receptors.

Supplementary Figure 7 Enrichment scores generally decrease when EBVS is applied to less accurate TAR ensembles.

a. ROC AUC and ROC(2%) scores for docking against individual conformers of the E0,4rdc ensemble, a randomly selected MD ensemble (E0,ran), and the lowest energy NOE-based structures for apo-TAR (PDB 1ANR) and tRNA (PDB 1EHZ) for the Full and Optimized libraries. Dashed lines indicate the values for the full ensemble. b. Dependence of the ROC AUC and ROC(2%) scores on ensemble size for the Full and Optimized libraries. c. Dependence of the ROC AUC and ROC(2%) scores on the RDC RMSD for the Full and Optimized libraries. d. Dependence of the ROC AUC and ROC(2%) scores on the RDC RMSD for the other 20-member ensembles of TAR. ROC plots were generated from one run of docking all molecules to all receptors. For b. c. and d. the mean and s.d. values over all possible sub-ensembles of each ensemble size are plotted.

Supplementary Figure 8 Each conformer of the ensemble contributes differentially to the small molecule scores and some hyper-enriching sub-ensembles outperform the N = 20 ensemble.

a. The Boltzmann-weighted average population of each conformer averaged across the Full library. b. The difference in the Boltzmann-weighted population of each conformer for each subset of molecules relative to the Full library. c. The percent of hyper-enriching sub-ensembles that contain each conformer for all libraries. (*) denotes conformers that most resemble ligand bound TAR conformations and (2) denotes conformers with two binding sites.

Supplementary Figure 9 Dependence of enrichment on TAR ensemble accuracy.

a. Dependence of the ROC AUC and ROC(2%) scores on the accuracy (RDC RMSD) of the TAR ensembles for the Full and Optimized libraries for all hits (blue) and cell-active hits (orange). All ROC values were generated from one run of docking all molecules to all receptors. b. Mean and s.d. of EBVS scores for hits (blue) and non-hits (gray) for all ensembles for the Full and Optimized libraries. Dashed lines represent the values for the E0,4rdc ensemble. c. Binding pocket volume (Å) and buriedness (ranging between 0.5–1.0 for fully open to fully occluded pockets, respectively) defined by ICM for each conformer of all TAR ensembles.

Supplementary Figure 10 RDC evaluation of ligand bound NMR structures and comparison of inter-helical angles for ligand bound NMR structures to EBVS predicted structures for E0,ran.

a. Agreement between NOE-based NMR structures and previously published RDCs using the best-fit order tensor determined using RAMAH. Red points denote bulge residues (U23-U25). The number of RDCs used in each correlation plot is n = 46, 30 and 40 for argininamide, acetylpromazine and Neomycin B, respectively. b. Comparison of inter-helical angles for the ligand-bound NMR structures (black, mean and s.d. values over all deposited structures) with conformers in the E0,ran ensemble (open squares) and the Boltzmann-weighted EBVS-predicted structures (red, mean and s.d. values over n = 20 independent docking runs).

Supplementary information

Supplementary Figures 1–10

Reporting Summary

Supplementary Tables and Notes

Supplementary Notes 1 and 2, and Supplementary Table 2.

Supplementary Table 1

TAR binders augmented with hits reported in the literature. Measured CD50 values represent the mean and s.d. from n = 3 independent experiments.

Supplementary Table 3

Table of PDB structures used in the ligand bound RNA docking benchmark.

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Ganser, L.R., Lee, J., Rangadurai, A. et al. High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble. Nat Struct Mol Biol 25, 425–434 (2018). https://doi.org/10.1038/s41594-018-0062-4

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