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 and Ulrik Lichtenberg
Additional contact information
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: 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 Lichtenberg: Global Research Technologies, Novo Nordisk A/S

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.

Date: 2022
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DOI: 10.1038/s41467-022-34031-z

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