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Deep learning to design nuclear-targeting abiotic miniproteins

Nature Chemistry volume 13, pages 992–1000 (2021)Cite this article

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Abstract

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence–activity predictions. The predicted miniproteins, termed ‘Mach’, reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.

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Fig. 1: Machine-learning model based on directed evolution predicts highly active abiotic miniproteins for macromolecule delivery.
Fig. 2: Machine-learning-based generator–predictor–optimizer loop predicts nuclear-targeting abiotic miniproteins.
Fig. 3: Interpretation of predictor CNN unveils activated substructures.
Fig. 4: Mach miniproteins are highly active in vitro and in vivo and deliver other biomacromolecules into the cytosol.

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Data availability

The main data supporting the findings of the current study are available within the paper and its Supplementary Information, which provides additional methods information, supplementary figures and data. Supplementary Table 1 includes sequences and activity of the modular library. Data used for training of the model is available at https://github.com/learningmatter-mit/peptimizer, and archived in the Zenodo repository53. Source data are provided with this paper.

Code availability

All the code used for model training and analysis is available at https://github.com/learningmatter-mit/peptimizer, and archived in Zenodo repository at https://zenodo.org/record/4815385#.YK_VCjZKhhE. Tutorial Jupyter notebooks are also in the repository, and demo Google Colab notebooks can be found at github.com/pikulsomesh/tutorials.

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Acknowledgements

We thank A. R. Loftis and J. Rodriguez for assistance with recombinant protein expression, C. Backlund for assistance with immunoassays, W. C. Salmon at the W. M. Keck Microscopy Facility at the Whitehead Institute for help with imaging, the Swanson Biotechnology Center Flow Cytometry Facility at the Koch Institute for the use of their flow cytometers and B. Mastis and S. Foley for help with the in vivo studies. We also thank Z.-N. Choo for igniting our interest in machine learning. This research was funded by Sarepta Therapeutics, by the MIT-SenseTime Alliance on Artificial Intelligence and by an award from the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic). C.K.S. (NSF Award no. 4000057398) acknowledges the National Science Foundation Graduate Research Fellowship (NSF grant no. 1122374) for research support.

Author information

Author notes

  1. Justin M. Wolfe

    Present address: Ra Pharmaceuticals, Cambridge, MA, USA

  2. Colin M. Fadzen

    Present address: Harvard Medical School, Boston, MA, USA

  3. These authors contributed equally: Carly K. Schissel, Somesh Mohapatra.

Authors and Affiliations

  1. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Carly K. Schissel, Justin M. Wolfe, Colin M. Fadzen, Andrei Loas & Bradley L. Pentelute

  2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Somesh Mohapatra & Rafael Gómez-Bombarelli

  3. Sarepta Therapeutics, Cambridge, MA, USA

    Kamela Bellovoda, Chia-Ling Wu, Jenna A. Wood & Annika B. Malmberg

  4. The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bradley L. Pentelute

  5. Center for Environmental Health Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bradley L. Pentelute

  6. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Bradley L. Pentelute

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Contributions

C.K.S., S.M., J.M.W., B.L.P. and R.G.-B. conceptualized the research. J.M.W. and C.M.F. synthesized and tested the modular library. S.M. and R.G.B. developed the machine learning model with input from C.K.S. and B.L.P. C.K.S. synthesized the Mach peptides and constructs, performed experiments and analysed the results. K.B., C.-L.W. and J.A.W. performed the in vivo study with input from A.B.M. C.K.S., S.M., A.L., B.L.P. and R.G.-B. wrote the manuscript with input from all the authors.

Corresponding authors

Correspondence to Rafael Gómez-Bombarelli or Bradley L. Pentelute.

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Competing interests

B.L.P. is a co-founder of Amide Technologies and of Resolute Bio. Both companies focus on the development of protein and peptide therapeutics. The following authors are inventors on patents and patent applications related to the technology described: J.M.W., C.M.F. and B.L.P are co-inventors on patents WO 2020028254A1 (6 February 2020), WO2019178479A1 (19 September 2019), WO2019079386A1 (25 April 2019) and WO2019079367A1 (24 April 2019), which describe trimeric peptides for antisense delivery, chimeric peptides for antisense delivery, CPPs for antisense delivery and bicyclic peptide oligonucleotide conjugates, respectively. A.B.M., K.B., C.-L.W. and J.A.W. are employees of Sarepta Therapeutics, and Sarepta Therapeutics provided a portion of the funding for the work.

Additional information

Peer review information Nature Chemistry thanks Dominik Heider, Ülo Langel, Zigang Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Discussion, Figs. 1–26, Tables 1–13 and Data.

Reporting Summary

Supplementary Data 1

Sequence and Activity information for peptides from the combinatorial library.

Source data

Source Data Fig. 1

Source Data and peptide sequences for Figure 1C.

Source Data Fig. 2

Source Data for scatterplots showing sequence vs activity in Figure 2C-E.

Source Data Fig. 3

Source Data for bar graph in Figure 3D.

Source Data Fig. 4

Source Data for graphs in Figure 4A-E and 4G-I.

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Schissel, C.K., Mohapatra, S., Wolfe, J.M. et al. Deep learning to design nuclear-targeting abiotic miniproteins. Nat. Chem. 13, 992–1000 (2021). https://doi.org/10.1038/s41557-021-00766-3

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