Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers
Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity r...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/141203.2 |
| _version_ | 1811088691150454784 |
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| author | López-Vidal, Eva M. Schissel, Carly K. Mohapatra, Somesh Bellovoda, Kamela Wu, Chia-Ling Wood, Jenna A. Malmberg, Annika B. Loas, Andrei Gómez-Bombarelli, Rafael Pentelute, Bradley L. |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering López-Vidal, Eva M. Schissel, Carly K. Mohapatra, Somesh Bellovoda, Kamela Wu, Chia-Ling Wood, Jenna A. Malmberg, Annika B. Loas, Andrei Gómez-Bombarelli, Rafael Pentelute, Bradley L. |
| author_sort | López-Vidal, Eva M. |
| collection | MIT |
| description | Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity relationships and inconsistent literature reports make it difficult to design effective CPPs for a given cargo. For example, polyarginine motifs are common in CPPs, promoting cell uptake at the expense of systemic toxicity. Machine learning may be able to address this challenge by bridging gaps between experimental data in order to discern sequence-activity relationships that evade our intuition. Our earlier data set and deep learning model led to the design of miniproteins (>40 amino acids) for antisense delivery. Here, we leveraged and expanded our model with data augmentation in the short CPP sequence space of the data set to extrapolate and discover short, low-arginine-content CPPs that would be easier to synthesize and amenable to rapid conjugation to desired cargo, and with minimal in vivo toxicity. The lead predicted peptide, termed P6, is as active as a polyarginine CPP for the delivery of an antisense oligomer, while having only one arginine side chain and 18 total residues. We determined the pentalysine motif and the C-terminal cysteine of P6 to be the main drivers of activity. The antisense conjugate was able to enhance corrective splicing in an animal model to produce functional eGFP in heart tissue in vivo while remaining nontoxic up to a dose of 60 mg/kg. In addition, P6 was able to deliver an enzyme to the cytosol of cells. Our findings suggest that, given a data set of long CPPs, we can discover by extrapolation short, active sequences that deliver antisense oligomers. |
| first_indexed | 2024-09-23T14:06:01Z |
| format | Article |
| id | mit-1721.1/141203.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:06:01Z |
| publishDate | 2022 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/141203.22024-06-04T18:51:15Z Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers López-Vidal, Eva M. Schissel, Carly K. Mohapatra, Somesh Bellovoda, Kamela Wu, Chia-Ling Wood, Jenna A. Malmberg, Annika B. Loas, Andrei Gómez-Bombarelli, Rafael Pentelute, Bradley L. Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Department of Chemistry Koch Institute for Integrative Cancer Research at MIT Massachusetts Institute of Technology. Center for Environmental Health Sciences Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity relationships and inconsistent literature reports make it difficult to design effective CPPs for a given cargo. For example, polyarginine motifs are common in CPPs, promoting cell uptake at the expense of systemic toxicity. Machine learning may be able to address this challenge by bridging gaps between experimental data in order to discern sequence-activity relationships that evade our intuition. Our earlier data set and deep learning model led to the design of miniproteins (>40 amino acids) for antisense delivery. Here, we leveraged and expanded our model with data augmentation in the short CPP sequence space of the data set to extrapolate and discover short, low-arginine-content CPPs that would be easier to synthesize and amenable to rapid conjugation to desired cargo, and with minimal in vivo toxicity. The lead predicted peptide, termed P6, is as active as a polyarginine CPP for the delivery of an antisense oligomer, while having only one arginine side chain and 18 total residues. We determined the pentalysine motif and the C-terminal cysteine of P6 to be the main drivers of activity. The antisense conjugate was able to enhance corrective splicing in an animal model to produce functional eGFP in heart tissue in vivo while remaining nontoxic up to a dose of 60 mg/kg. In addition, P6 was able to deliver an enzyme to the cytosol of cells. Our findings suggest that, given a data set of long CPPs, we can discover by extrapolation short, active sequences that deliver antisense oligomers. 2022-06-13T18:59:16Z 2022-03-15T19:00:15Z 2022-06-13T18:59:16Z 2021-10 2021-07 2022-03-15T18:58:20Z Article http://purl.org/eprint/type/JournalArticle 2691-3704 https://hdl.handle.net/1721.1/141203.2 López-Vidal, Eva M, Schissel, Carly K, Mohapatra, Somesh, Bellovoda, Kamela, Wu, Chia-Ling et al. 2021. "Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers." JACS Au, 1 (11). en http://dx.doi.org/10.1021/jacsau.1c00327 JACS Au Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/octet-stream American Chemical Society (ACS) ACS |
| spellingShingle | López-Vidal, Eva M. Schissel, Carly K. Mohapatra, Somesh Bellovoda, Kamela Wu, Chia-Ling Wood, Jenna A. Malmberg, Annika B. Loas, Andrei Gómez-Bombarelli, Rafael Pentelute, Bradley L. Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title | Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title_full | Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title_fullStr | Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title_full_unstemmed | Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title_short | Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers |
| title_sort | deep learning enables discovery of a short nuclear targeting peptide for efficient delivery of antisense oligomers |
| url | https://hdl.handle.net/1721.1/141203.2 |
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