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Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

[Image: see text] Cell-penetrating peptides (CPPs) can facilitate the intracellular delivery of large therapeutically relevant molecules, including proteins and oligonucleotides. Although hundreds of CPP sequences are described in the literature, predicting efficacious sequences remains difficult. H...

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Autores principales: Wolfe, Justin M., Fadzen, Colin M., Choo, Zi-Ning, Holden, Rebecca L., Yao, Monica, Hanson, Gunnar J., Pentelute, Bradley L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920612/
https://www.ncbi.nlm.nih.gov/pubmed/29721534
http://dx.doi.org/10.1021/acscentsci.8b00098
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author Wolfe, Justin M.
Fadzen, Colin M.
Choo, Zi-Ning
Holden, Rebecca L.
Yao, Monica
Hanson, Gunnar J.
Pentelute, Bradley L.
author_facet Wolfe, Justin M.
Fadzen, Colin M.
Choo, Zi-Ning
Holden, Rebecca L.
Yao, Monica
Hanson, Gunnar J.
Pentelute, Bradley L.
author_sort Wolfe, Justin M.
collection PubMed
description [Image: see text] Cell-penetrating peptides (CPPs) can facilitate the intracellular delivery of large therapeutically relevant molecules, including proteins and oligonucleotides. Although hundreds of CPP sequences are described in the literature, predicting efficacious sequences remains difficult. Here, we focus specifically on predicting CPPs for the delivery of phosphorodiamidate morpholino oligonucleotides (PMOs), a compelling type of antisense therapeutic that has recently been FDA approved for the treatment of Duchenne muscular dystrophy. Using literature CPP sequences, 64 covalent PMO–CPP conjugates were synthesized and evaluated in a fluorescence-based reporter assay for PMO activity. Significant discrepancies were observed between the sequences that performed well in this assay and the sequences that performed well when conjugated to only a small-molecule fluorophore. As a result, we envisioned that our PMO–CPP library would be a useful training set for a computational model to predict CPPs for PMO delivery. We used the PMO activity data to fit a random decision forest classifier to predict whether or not covalent attachment of a given peptide would enhance PMO activity at least 3-fold. To validate the model experimentally, seven novel sequences were generated, synthesized, and tested in the fluorescence reporter assay. All computationally predicted positive sequences were positive in the assay, and one sequence performed better than 80% of the tested literature CPPs. These results demonstrate the power of machine learning algorithms to identify peptide sequences with particular functions and illustrate the importance of tailoring a CPP sequence to the cargo of interest.
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spelling pubmed-59206122018-05-02 Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery Wolfe, Justin M. Fadzen, Colin M. Choo, Zi-Ning Holden, Rebecca L. Yao, Monica Hanson, Gunnar J. Pentelute, Bradley L. ACS Cent Sci [Image: see text] Cell-penetrating peptides (CPPs) can facilitate the intracellular delivery of large therapeutically relevant molecules, including proteins and oligonucleotides. Although hundreds of CPP sequences are described in the literature, predicting efficacious sequences remains difficult. Here, we focus specifically on predicting CPPs for the delivery of phosphorodiamidate morpholino oligonucleotides (PMOs), a compelling type of antisense therapeutic that has recently been FDA approved for the treatment of Duchenne muscular dystrophy. Using literature CPP sequences, 64 covalent PMO–CPP conjugates were synthesized and evaluated in a fluorescence-based reporter assay for PMO activity. Significant discrepancies were observed between the sequences that performed well in this assay and the sequences that performed well when conjugated to only a small-molecule fluorophore. As a result, we envisioned that our PMO–CPP library would be a useful training set for a computational model to predict CPPs for PMO delivery. We used the PMO activity data to fit a random decision forest classifier to predict whether or not covalent attachment of a given peptide would enhance PMO activity at least 3-fold. To validate the model experimentally, seven novel sequences were generated, synthesized, and tested in the fluorescence reporter assay. All computationally predicted positive sequences were positive in the assay, and one sequence performed better than 80% of the tested literature CPPs. These results demonstrate the power of machine learning algorithms to identify peptide sequences with particular functions and illustrate the importance of tailoring a CPP sequence to the cargo of interest. American Chemical Society 2018-04-05 2018-04-25 /pmc/articles/PMC5920612/ /pubmed/29721534 http://dx.doi.org/10.1021/acscentsci.8b00098 Text en Copyright © 2018 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Wolfe, Justin M.
Fadzen, Colin M.
Choo, Zi-Ning
Holden, Rebecca L.
Yao, Monica
Hanson, Gunnar J.
Pentelute, Bradley L.
Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title_full Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title_fullStr Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title_full_unstemmed Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title_short Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery
title_sort machine learning to predict cell-penetrating peptides for antisense delivery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920612/
https://www.ncbi.nlm.nih.gov/pubmed/29721534
http://dx.doi.org/10.1021/acscentsci.8b00098
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