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Discovering de novo peptide substrates for enzymes using machine learning

The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative...

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Autores principales: Tallorin, Lorillee, Wang, JiaLei, Kim, Woojoo E., Sahu, Swagat, Kosa, Nicolas M., Yang, Pu, Thompson, Matthew, Gilson, Michael K., Frazier, Peter I., Burkart, Michael D., Gianneschi, Nathan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286390/
https://www.ncbi.nlm.nih.gov/pubmed/30531862
http://dx.doi.org/10.1038/s41467-018-07717-6
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author Tallorin, Lorillee
Wang, JiaLei
Kim, Woojoo E.
Sahu, Swagat
Kosa, Nicolas M.
Yang, Pu
Thompson, Matthew
Gilson, Michael K.
Frazier, Peter I.
Burkart, Michael D.
Gianneschi, Nathan C.
author_facet Tallorin, Lorillee
Wang, JiaLei
Kim, Woojoo E.
Sahu, Swagat
Kosa, Nicolas M.
Yang, Pu
Thompson, Matthew
Gilson, Michael K.
Frazier, Peter I.
Burkart, Michael D.
Gianneschi, Nathan C.
author_sort Tallorin, Lorillee
collection PubMed
description The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative machine learning process by which experimental data is deposited into a mathematical algorithm that selects potential peptide substrates to be tested experimentally. Once tested, the algorithm uses the experimental data to refine future selections. This process is repeated until a suitable set of de novo peptide substrates are discovered. We employed this technology to discover orthogonal peptide substrates for 4’-phosphopantetheinyl transferase, an enzyme class that covalently modifies proteins. In this manner, we have demonstrated that machine learning can be leveraged to guide peptide optimization for specific biochemical functions not immediately accessible by biological screening techniques, such as phage display and random mutagenesis.
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spelling pubmed-62863902018-12-11 Discovering de novo peptide substrates for enzymes using machine learning Tallorin, Lorillee Wang, JiaLei Kim, Woojoo E. Sahu, Swagat Kosa, Nicolas M. Yang, Pu Thompson, Matthew Gilson, Michael K. Frazier, Peter I. Burkart, Michael D. Gianneschi, Nathan C. Nat Commun Article The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative machine learning process by which experimental data is deposited into a mathematical algorithm that selects potential peptide substrates to be tested experimentally. Once tested, the algorithm uses the experimental data to refine future selections. This process is repeated until a suitable set of de novo peptide substrates are discovered. We employed this technology to discover orthogonal peptide substrates for 4’-phosphopantetheinyl transferase, an enzyme class that covalently modifies proteins. In this manner, we have demonstrated that machine learning can be leveraged to guide peptide optimization for specific biochemical functions not immediately accessible by biological screening techniques, such as phage display and random mutagenesis. Nature Publishing Group UK 2018-12-07 /pmc/articles/PMC6286390/ /pubmed/30531862 http://dx.doi.org/10.1038/s41467-018-07717-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tallorin, Lorillee
Wang, JiaLei
Kim, Woojoo E.
Sahu, Swagat
Kosa, Nicolas M.
Yang, Pu
Thompson, Matthew
Gilson, Michael K.
Frazier, Peter I.
Burkart, Michael D.
Gianneschi, Nathan C.
Discovering de novo peptide substrates for enzymes using machine learning
title Discovering de novo peptide substrates for enzymes using machine learning
title_full Discovering de novo peptide substrates for enzymes using machine learning
title_fullStr Discovering de novo peptide substrates for enzymes using machine learning
title_full_unstemmed Discovering de novo peptide substrates for enzymes using machine learning
title_short Discovering de novo peptide substrates for enzymes using machine learning
title_sort discovering de novo peptide substrates for enzymes using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286390/
https://www.ncbi.nlm.nih.gov/pubmed/30531862
http://dx.doi.org/10.1038/s41467-018-07717-6
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