Author
Listed:
- 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
Northwestern University)
- 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
Northwestern University)
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.
Suggested Citation
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 & Nathan C. Gianneschi, 2018.
"Discovering de novo peptide substrates for enzymes using machine learning,"
Nature Communications, Nature, vol. 9(1), pages 1-10, December.
Handle:
RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07717-6
DOI: 10.1038/s41467-018-07717-6
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07717-6. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.