1. Combining mass spectrometry and machine learning to discover bioactive peptides
- Author
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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 de Lichtenberg
- Subjects
Machine Learning ,Mammals ,Mice ,Multidisciplinary ,Humans ,Animals ,General Physics and Astronomy ,General Chemistry ,Peptides ,Mass Spectrometry ,General Biochemistry, Genetics and Molecular Biology - 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.
- Published
- 2022