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Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

View ORCID ProfileCarly K. Schissel, View ORCID ProfileSomesh Mohapatra, View ORCID ProfileJustin M. Wolfe, View ORCID ProfileColin M. Fadzen, View ORCID ProfileKamela Bellovoda, Chia-Ling Wu, Jenna A. Wood, Annika B. Malmberg, View ORCID ProfileAndrei Loas, View ORCID ProfileRafael Gómez-Bombarelli, View ORCID ProfileBradley L. Pentelute
doi: https://doi.org/10.1101/2020.04.10.036566
Carly K. Schissel
1Massachusetts Institute of Technology, Department of Chemistry, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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  • ORCID record for Carly K. Schissel
Somesh Mohapatra
2Massachusetts Institute of Technology, Department of Materials Science and Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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Justin M. Wolfe
1Massachusetts Institute of Technology, Department of Chemistry, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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Colin M. Fadzen
1Massachusetts Institute of Technology, Department of Chemistry, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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Kamela Bellovoda
3Sarepta Therapeutics, 215 First Street, Cambridge, MA 02142, USA
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Chia-Ling Wu
3Sarepta Therapeutics, 215 First Street, Cambridge, MA 02142, USA
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Jenna A. Wood
3Sarepta Therapeutics, 215 First Street, Cambridge, MA 02142, USA
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Annika B. Malmberg
3Sarepta Therapeutics, 215 First Street, Cambridge, MA 02142, USA
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Andrei Loas
1Massachusetts Institute of Technology, Department of Chemistry, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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  • ORCID record for Andrei Loas
Rafael Gómez-Bombarelli
2Massachusetts Institute of Technology, Department of Materials Science and Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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  • For correspondence: blp@mit.edu rafagb@mit.edu
Bradley L. Pentelute
1Massachusetts Institute of Technology, Department of Chemistry, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
4The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Cambridge, MA 02142, USA
5Center for Environmental Health Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
6Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
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  • For correspondence: blp@mit.edu rafagb@mit.edu
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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 couple supervised and unsupervised deep learning with high-throughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequence-activity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases.

One sentence summary Deep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus.

Competing Interest Statement

Bradley L. Pentelute is a co-founder of Amide Technologies and 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: Justin M. Wolfe, Colin M. Fadzen and Bradley L. Pentelute are co-inventors on patents WO 2020028254A1 (February 6, 2020), WO2019178479A1 (September 19, 2019), WO2019079386A1 (April 25, 2019), and WO2019079367A1 (April 24, 2019), describing trimeric peptides for antisense delivery, chimeric peptides for antisense delivery, cell-penetrating peptides for antisense delivery, and bicyclic peptide oligonucleotide conjugates, respectively. MIT and Sarepta Therapeutics have filed a provisional patent application related to the composition of materials described in this work.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 13, 2020.
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Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers
Carly K. Schissel, Somesh Mohapatra, Justin M. Wolfe, Colin M. Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna A. Wood, Annika B. Malmberg, Andrei Loas, Rafael Gómez-Bombarelli, Bradley L. Pentelute
bioRxiv 2020.04.10.036566; doi: https://doi.org/10.1101/2020.04.10.036566
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Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers
Carly K. Schissel, Somesh Mohapatra, Justin M. Wolfe, Colin M. Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna A. Wood, Annika B. Malmberg, Andrei Loas, Rafael Gómez-Bombarelli, Bradley L. Pentelute
bioRxiv 2020.04.10.036566; doi: https://doi.org/10.1101/2020.04.10.036566

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