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Deep learning to design nuclear-targeting abiotic miniproteins
Name
nihms-1773779.pdf
Description
Accepted version
Size
998.41 KB
Format
Adobe PDF
Checksum (MD5)
cbf350e04f247e0a2a21d709ea2116a6
Author(s)
Schissel, Carly K
Mohapatra, Somesh
Wolfe, Justin M
Fadzen, Colin M
Bellovoda, Kamela
Wu, Chia-Ling
Wood, Jenna A
Malmberg, Annika B
Loas, Andrei
Gómez-Bombarelli, Rafael
Date Issued
2021
Journal
Nature Chemistry
Publisher
Springer Science and Business Media LLC
Citation
Schissel, Carly K, Mohapatra, Somesh, Wolfe, Justin M, Fadzen, Colin M, Bellovoda, Kamela et al. 2021. "Deep learning to design nuclear-targeting abiotic miniproteins." Nature Chemistry, 13 (10).
Version
Author's final manuscript
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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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1038/S41557-021-00766-3
