Nature Communications (Oct 2022)
Combining mass spectrometry and machine learning to discover bioactive peptides
- Christian T. Madsen,
- Jan C. Refsgaard,
- Felix G. Teufel,
- Sonny K. Kjærulff,
- Zhe Wang,
- Guangjun Meng,
- Carsten Jessen,
- Petteri Heljo,
- Qunfeng Jiang,
- Xin Zhao,
- Bo Wu,
- Xueping Zhou,
- Yang Tang,
- Jacob F. Jeppesen,
- Christian D. Kelstrup,
- Stephen T. Buckley,
- Søren Tullin,
- Jan Nygaard-Jensen,
- Xiaoli Chen,
- Fang Zhang,
- Jesper V. Olsen,
- Dan Han,
- Mads Grønborg,
- Ulrik de Lichtenberg
Affiliations
- Christian T. Madsen
- Global Research Technologies, Novo Nordisk A/S
- Jan C. Refsgaard
- Global Research Technologies, Novo Nordisk A/S
- Felix G. Teufel
- Global Research Technologies, Novo Nordisk A/S
- Sonny K. Kjærulff
- Global Research Technologies, Novo Nordisk A/S
- Zhe Wang
- Novo Nordisk Research Centre China
- Guangjun Meng
- Novo Nordisk Research Centre China
- Carsten Jessen
- Global Research Technologies, Novo Nordisk A/S
- Petteri Heljo
- Global Research Technologies, Novo Nordisk A/S
- Qunfeng Jiang
- Novo Nordisk Research Centre China
- Xin Zhao
- Novo Nordisk Research Centre China
- Bo Wu
- Novo Nordisk Research Centre China
- Xueping Zhou
- Novo Nordisk Research Centre China
- Yang Tang
- Novo Nordisk Research Centre China
- Jacob F. Jeppesen
- Global Research Technologies, Novo Nordisk A/S
- Christian D. Kelstrup
- Global Research Technologies, Novo Nordisk A/S
- Stephen T. Buckley
- Global Research Technologies, Novo Nordisk A/S
- Søren Tullin
- Global Research Technologies, Novo Nordisk A/S
- Jan Nygaard-Jensen
- Global Research Technologies, Novo Nordisk A/S
- Xiaoli Chen
- Novo Nordisk Research Centre China
- Fang Zhang
- Novo Nordisk Research Centre China
- Jesper V. Olsen
- Department of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen
- Dan Han
- Novo Nordisk Research Centre China
- Mads Grønborg
- Global Research Technologies, Novo Nordisk A/S
- Ulrik de Lichtenberg
- Global Research Technologies, Novo Nordisk A/S
- DOI
- https://doi.org/10.1038/s41467-022-34031-z
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 17
Abstract
Bioactive peptides regulate many physiological functions but progress in discovering them has been slow. Here, the authors use a machine learning framework to predict mammalian peptide candidates from the global and local structure of large-scale tissue-specific mass spectrometry data.
