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Deep generative design of RNA aptamers using structural predictions

Abstract

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

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Fig. 1: A deep learning approach for the 3D-structure-based generative design of RNAs.
Fig. 2: Experimental validation, optimization and mechanism of fluorescence of generated light-up RNA aptamers.

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Data availability

The numerical data supporting the findings of this paper are provided in the Source Data, and the sequences can be generated by running RhoDesign. Source Data for Figs. 1 and 2 are available. Sequences generated from RhoDesign and accompanying data are available as Supplementary Data 1. The training dataset and model checkpoints for RhoDesign are available from Zenodo60. The PDB structure for Mango-III (A10U), 6UP0, is available from the PDB61.

Code availability

RhoDesign is available at https://github.com/ml4bio/RhoDesign and from Zenodo60.

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Acknowledgements

This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award K25AI168451 (to F.W.), the Swiss National Science Foundation under grant number SNSF_ 203071 (to A.K.), the National Science Foundation Graduate Research Fellowship (to A.Z.W.), the Research Grants Council of the Hong Kong Special Administrative Region, China (projects CUHK 14222922 and RGC GRF 2151185 to I.K. and project CUHK 24204023 to Y.L.), a grant from the Innovation and Technology Commission of the Hong Kong Special Administrative Region, China (projects GHP/065/21SZ, IDBF24ENG06 and ITS/247/23FP to Y.L.), the National Key R&D Program of China (project 2022ZD0160101 to Y.L.) and the Broad Institute of MIT and Harvard (to J.J.C.). This work is part of the Antibiotics-AI Project, which is directed by J.J.C. and supported by the Audacious Project, Flu Lab, LLC, the Sea Grape Foundation, R. Zander and H. Wyss for the Wyss Foundation, and an anonymous donor. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Felix Wong, Dongchen He.

Authors and Affiliations

  1. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Felix Wong, Aarti Krishnan, Alexander Z. Wang, Satotaka Omori, Yu Li & James J. Collins

  2. Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Felix Wong, Aarti Krishnan, Alexander Z. Wang, Tianqing Zhang, Katherine Ilia, Jack X. Chen, Yu Li & James J. Collins

  3. Integrated Biosciences, Redwood City, CA, USA

    Felix Wong, Satotaka Omori & Alicia Li

  4. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

    Dongchen He, Liang Hong, Jiuming Wang, Zhihang Hu, Qinze Yu, Irwin King & Yu Li

  5. Shanghai Artificial Intelligence Laboratory, Shanghai, China

    Dongchen He

  6. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Jiahua Rao

  7. Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Wengong Jin

  8. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Wengong Jin

  9. Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China

    Shuangjia Zheng

  10. The CUHK Shenzhen Research Institute, Shenzhen, China

    Yu Li

  11. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Yu Li & James J. Collins

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Contributions

F.W. conceived research, performed or directed all experiments, wrote the paper and supervised research. D.H. and L.H. developed RhoDesign and performed computational analyses, with contributions from J.W., Z.H., Q.Y. and I.K. A.K. and A.Z.W. conceived research and performed experiments and analyses. S.O. and A.L. performed experiments. J.R., W.J., T.Z., K.I. and J.X.C. performed analyses. S.Z. conceived research and performed analyses. Y.L. conceived research, performed or directed all analyses and supervised research. J.J.C. conceived and supervised research. All authors assisted with manuscript editing.

Corresponding authors

Correspondence to
Yu Li or James J. Collins.

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Competing interests

J.J.C. is the founding scientific advisory board chair of Integrated Biosciences. F.W. is a co-founder of Integrated Biosciences. S.O. has an equity interest in Integrated Biosciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Jianyi Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Structural fidelity of RhoFold’s Mango-III prediction, conserved sequence motifs in Mango aptamers, and comparison to aptamers 1-4.

a, (Left) RhoFold-predicted 3D structure of Mango-III, aligned to the ground truth structure for Mango-III in PDB 6UP0. (Right) AlphaFold 3-predicted 3D structure of Mango-III, aligned to the ground truth structure for Mango-III in PDB 6UP0. b, Comparison of aptamers 1-4’s sequences against Mango sequences. Here, conserved sequence motifs in Mango aptamers are indicated in red.

Extended Data Fig. 2 AlphaFold 3-predicted 3D and RhoFold-predicted secondary structures.

a, Predicted 3D structures for aptamers 1-4 generated using AlphaFold 3, as detailed in the Methods—RNA 3D structure prediction. RMSD, TM-score, and pLDDT values for each structure as compared to the ground truth structure for Mango-III in PDB 6UP0 are shown. b, Secondary structures for Mango-III and aptamers 1-4, as generated based on the corresponding PDB structure (6UP0; Mango-III) or RhoFold predictions (aptamers 1-4), as detailed in the Methods—RNA secondary structure prediction.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1–3.

Reporting Summary

Peer Review File

Supplementary Data 1

Generated and tested RNA sequences, in addition to model predictions of fluorescence activity.

Source data

Source Data Figs. 1 and 2

Statistical source data for Figs. 1 and 2.

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Wong, F., He, D., Krishnan, A. et al. Deep generative design of RNA aptamers using structural predictions.
Nat Comput Sci (2024). https://doi.org/10.1038/s43588-024-00720-6

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  • Received: 22 February 2024

  • Accepted: 07 October 2024

  • Published: 06 November 2024

  • DOI: https://doi.org/10.1038/s43588-024-00720-6

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