Deep learning to design nuclear-targeting abiotic miniproteins
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; Pentelute, Bradley L.; ... Show more Show less
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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.
Springer Science and Business Media LLC
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).
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