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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nature communications Jg. 9; H. 1; S. 5253 - 10
Hauptverfasser: 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.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 07.12.2018
Nature Publishing Group
Nature Portfolio
Schlagworte:
49
49/75
631/114/1305
631/61
631/92
Algorithms
Artificial intelligence
Biochemistry
Computer applications
Enzymes
Experimental data
Humanities and Social Sciences
Iterative methods
Learning algorithms
Machine learning
multidisciplinary
Organic chemistry
Peptides
Phage display
Phages
Proteins
Random mutagenesis
Science
Science (multidisciplinary)
Substrates
ISSN:2041-1723, 2041-1723
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. The discovery of peptide substrates for enzymes with selective activities is a central goal in chemical biology. Here, the authors develop a hybrid method combining machine learning and experimental testing for fast optimization of peptides for specific, orthogononal functions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-07717-6