Discovering de novo peptide substrates for enzymes using machine learning

Lorillee Tallorin, JiaLei Wang, Woojoo E. Kim, Swagat Sahu, Nicolas M. Kosa, Pu Yang, Matthew Thompson, Michael K. Gilson, Peter I. Frazier (), Michael D. Burkart () and Nathan C. Gianneschi ()
Additional contact information
Lorillee Tallorin: University of California San Diego
JiaLei Wang: Cornell University
Woojoo E. Kim: University of California San Diego
Swagat Sahu: University of California San Diego
Nicolas M. Kosa: University of California San Diego
Pu Yang: Cornell University
Matthew Thompson: University of California San Diego
Michael K. Gilson: University of California San Diego
Peter I. Frazier: Cornell University
Michael D. Burkart: University of California San Diego
Nathan C. Gianneschi: University of California San Diego

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07717-6

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DOI: 10.1038/s41467-018-07717-6

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