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Deep generative design of RNA aptamers using structural predictions

Deep generative design of RNA aptamers using structural predictions
  • Cech, T. R., Zaug, A. J. & Grabowski, P. J. In vitro splicing of the ribosomal RNA precursor of Tetrahymena: involvement of a guanosine nucleotide in the excision of the intervening sequence. Cell 27, 487–496 (1981).

    Article 

    Google Scholar 

  • Guerrier-Takada, C., Gardiner, K., Marsh, T., Pace, N. & Altman, S. The RNA moiety of ribonuclease P is the catalytic subunit of the enzyme. Cell 35, 849–857 (1983).

    Article 

    Google Scholar 

  • Statello, L., Guo, C.-J., Chen, L.-L. & Huarte, M. Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol. 22, 96–118 (2021).

    Article 

    Google Scholar 

  • Dinger, M. E., Mercer, T. R. & Mattick, J. S. RNAs as extracellular signaling molecules. J. Mol. Endocrinol. 40, 151–159 (2008).

    Article 

    Google Scholar 

  • Keefe, A. D., Pai, S. & Ellington, A. Aptamers as therapeutics. Nat. Rev. Drug. Discov. 9, 537–550 (2010).

    Article 

    Google Scholar 

  • Tuerk, C., MacDougal, S. & Gold, L. RNA pseudoknots that inhibit human immunodeficiency virus type 1 reverse transcriptase. Proc. Natl. Acad. Sci. USA 89, 6988–6992 (1992).

    Article 

    Google Scholar 

  • Pardee, K. et al. Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell 165, 1255–1266 (2016).

    Article 

    Google Scholar 

  • Angenent-Mari, N. M., Garruss, A. S., Soenksen, L. R., Church, G. & Collins, J. J. A deep learning approach to programmable RNA switches. Nat. Commun. 11, 5057 (2020).

    Article 

    Google Scholar 

  • Valeri, J. A. et al. Sequence-to-function deep learning frameworks for engineered riboregulators. Nat. Commun. 11, 5058 (2020).

    Article 

    Google Scholar 

  • Takahashi, M. K. et al. A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat. Commun. 9, 3347 (2018).

    Article 

    Google Scholar 

  • Green, A. A., Silver, P. A., Collins, J. J. & Yin, P. Toehold switches: de-novo-designed regulators of gene expression. Cell 159, 925–939 (2014).

    Article 

    Google Scholar 

  • Paige, J. S., Wu, K. Y. & Jaffrey, S. R. RNA mimics of green fluorescent protein. Science 333, 642–646 (2011).

    Article 

    Google Scholar 

  • Miao, Z. & Westhof, E. RNA structure: advances and assessment of 3D structure prediction. Annu. Rev. Biophys. 46, 483–503 (2017).

    Article 

    Google Scholar 

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 

    Google Scholar 

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 

    Google Scholar 

  • Shen, T. et al. E2Efold-3D: end-to-end deep learning method for accurate de novo RNA 3D structure prediction. Preprint at (2022).

  • Wang, W. et al. trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Nat. Commun. 14, 7266 (2023).

    Article 

    Google Scholar 

  • Pearce, R., Li, Y., Omenn, G. S. & Zhang, Y. Fast and accurate ab initio protein structure prediction using deep learning potentials. PLoS Comput. Biol. 18, e1010539 (2022).

    Article 

    Google Scholar 

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 

    Google Scholar 

  • Das, R. et al. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 91, 1747–1770 (2023).

    Article 

    Google Scholar 

  • Runge, F., Stoll, D., Falkner, S. & Hutter, F. Learning to design RNA. In International Conference on Learning Representations 2019 (ICLR, 2019).

  • Wu, M. J., Andreasson, J. O. L., Kladwang, W., Greenleaf, W. & Das, R. Automated design of diverse stand-alone riboswitches. ACS Synth. Biol. 8, 1838–1846 (2019).

    Article 

    Google Scholar 

  • Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article 

    Google Scholar 

  • Jing, B. et al. Learning from protein structure with geometric vector perceptrons. In International Conference on Learning Representations (ICLR, 2021).

  • Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 5998–6008 (NIPS, 2017).

  • Hsu, C. et al. Learning inverse folding from millions of predicted structures. Proc. Mach. Learn. Res. 162, 8946–8970 (2022).

    Google Scholar 

  • Yang, X., Yoshizoe, K., Taneda, A. & Tsuda, K. RNA inverse folding using Monte Carlo tree search. BMC Bioinform. 18, 468 (2017).

    Article 

    Google Scholar 

  • Joshi, C. K. & Liò, P. gRNAde: a geometric deep learning for 3D RNA inverse design. Methods Mol. Biol. 2847, 121–135 (2025).

    Article 

    Google Scholar 

  • Tan, C. et al. RDesign: hierarchical data-efficient representation learning for tertiary structure-based RNA design. In The Twelfth International Conference on Learning Representations (ICLR, 2024).

  • Rubio-Largo, Á., Lozano-García, N., Granado-Criado, J. & Vega-Rodríguez, M. A. Solving the RNA inverse folding problem through target structure decomposition and multiobjective evolutionary computation. Appl. Soft Comput. 147, 110779 (2023).

    Article 

    Google Scholar 

  • Autour, A. et al. Fluorogenic RNA Mango aptamers for imaging small non-coding RNAs in mammalian cells. Nat. Commun. 9, 656 (2018).

    Article 

    Google Scholar 

  • Jeng, S. C. Y. et al. Fluorogenic aptamers resolve the flexibility of RNA junctions using orientation-dependent FRET. RNA 27, 433–444 (2021).

    Article 

    Google Scholar 

  • Iwano, N. et al. Generative aptamer discovery using RaptGen. Nat. Comput. Sci. 2, 378–386 (2022).

    Article 

    Google Scholar 

  • Jiang, P. et al. MPBind: a meta-motif-based statistical framework and pipeline to predict binding potential of SELEX-derived aptamers. Bioinformatics 30, 2665–2667 (2014).

    Article 

    Google Scholar 

  • Jeng, S. C., Chan, H. H., Booy, E. P., McKenna, S. A. & Unrau, P. J. Fluorophore ligand binding and complex stabilization of the RNA Mango and RNA Spinach aptamers. RNA 22, 1884–1892 (2016).

    Article 

    Google Scholar 

  • Trachman, R. J. III et al. Structural basis for high-affinity fluorophore binding and activation by RNA Mango. Nat. Chem. Biol. 13, 807–813 (2017).

    Article 

    Google Scholar 

  • Liu, L. Y., Ma, T. Z., Zeng, Y. L., Liu, W. & Mao, Z. W. Structural basis of pyridostatin and its derivatives specifically binding to G-quadruplexes. J. Am. Chem. Soc. 144, 11878–11887 (2022).

    Article 

    Google Scholar 

  • Han, F. X., Wheelhouse, R. T. & Hurley, L. H. Interactions of TMPyP4 and TMPyP2 with quadruplex DNA. Structural basis for the differential effects on telomerase inhibition. J. Am. Chem. Soc. 121, 3561–3570 (1999).

    Article 

    Google Scholar 

  • Rocca, R. et al. Molecular recognition of a carboxy pyridostatin toward G-quadruplex structures: why does it prefer RNA? Chem. Biol. Drug Des. 90, 919–925 (2017).

    Article 

    Google Scholar 

  • Chen, X. C. et al. Tracking the dynamic folding and unfolding of RNA G-quadruplexes in live cells. Angew. Chem. Int. Ed. Engl. 57, 4702–4706 (2018).

    Article 

    Google Scholar 

  • Ellington, A. D. & Szostak, J. W. In vitro selection of RNA molecules that bind specific ligands. Nature 346, 818–822 (1990).

    Article 

    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).

    Google Scholar 

  • The RNAcentral Consortium. RNAcentral: a hub of information for non-coding RNA sequences. Nucleic Acids Res. 47, D221–D229 (2019).

    Article 

    Google Scholar 

  • Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).

    Article 

    Google Scholar 

  • Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26, 680–682 (2010).

    Article 

    Google Scholar 

  • Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109–1115 (2022).

    Article 

    Google Scholar 

  • Huang, P.-S., Boyken, S. E. & Baker, D. The coming of age of de novo protein design. Nature 537, 320–327 (2016).

    Article 

    Google Scholar 

  • Boniecki, M. J. et al. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 44, e63 (2016).

    Article 

    Google Scholar 

  • Li, Y. et al. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nat. Commun. 14, 5745 (2023).

    Article 

    Google Scholar 

  • Biesiada, M. et al. Automated RNA 3D structure prediction with RNAComposer. Methods Mol. Biol. 1490, 199–215 (2016).

    Article 

    Google Scholar 

  • Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).

    Article 

    Google Scholar 

  • Case, D. A. et al. AmberTools. J. Chem. Inf. Model. 63, 6183–6191 (2023).

    Article 

    Google Scholar 

  • Zok, T. et al. RNApdbee 2.0: multifunctional tool for RNA structure annotation. Nucleic Acids Res. 46, W30–W35 (2018).

    Article 

    Google Scholar 

  • Fu, L. et al. UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic Acids Res. 50, e14 (2022).

    Article 

    Google Scholar 

  • Lorenz, R. et al. ViennaRNA package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    Article 

    Google Scholar 

  • Sato, K., Akiyama, M. & Sakakibara, Y. RNA secondary structure prediction using deep learning with thermodynamic integration. Nat. Commun. 12, 941 (2021).

    Article 

    Google Scholar 

  • Chen, J. et al. Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. Preprint at (2022).

  • Wong, F. et al. Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Mol. Syst. Biol. 18, e11081 (2022).

    Article 

    Google Scholar 

  • Trachman, R. J. III et al. Structure and functional reselection of the Mango-III fluorogenic RNA aptamer. Nat. Chem. Biol. 15, 472–479 (2019).

    Article 

    Google Scholar 

  • Wong, F. et al. Supporting code for: Deep generative design of RNA aptamers using structural predictions. Zenodo (2024).

  • Trachman, R. J. & Ferre-D’Amare, A. R. Structure of the Mango-III fluorescent aptamer bound to YO3-biotin. Protein Data Bank (2019).

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