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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Vydané v:Nature communications Ročník 9; číslo 1; s. 5253 - 10
Hlavní autori: 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.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 07.12.2018
Nature Publishing Group
Nature Portfolio
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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
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Shrnutí: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.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-07717-6