Combining mass spectrometry and machine learning to discover bioactive peptides

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Combining mass spectrometry and machine learning to discover bioactive peptides. / Madsen, Christian T.; Refsgaard, Jan C.; Teufel, Felix G.; Kjærulff, Sonny K.; Wang, Zhe; Meng, Guangjun; Jessen, Carsten; Heljo, Petteri; Jiang, Qunfeng; Zhao, Xin; Wu, Bo; Zhou, Xueping; Tang, Yang; Jeppesen, Jacob F.; Kelstrup, Christian D.; Buckley, Stephen T.; Tullin, Søren; Nygaard-Jensen, Jan; Chen, Xiaoli; Zhang, Fang; Olsen, Jesper V.; Han, Dan; Grønborg, Mads; de Lichtenberg, Ulrik.

I: Nature Communications, Bind 13, 6235, 2022.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Madsen, CT, Refsgaard, JC, Teufel, FG, Kjærulff, SK, Wang, Z, Meng, G, Jessen, C, Heljo, P, Jiang, Q, Zhao, X, Wu, B, Zhou, X, Tang, Y, Jeppesen, JF, Kelstrup, CD, Buckley, ST, Tullin, S, Nygaard-Jensen, J, Chen, X, Zhang, F, Olsen, JV, Han, D, Grønborg, M & de Lichtenberg, U 2022, 'Combining mass spectrometry and machine learning to discover bioactive peptides', Nature Communications, bind 13, 6235. https://doi.org/10.1038/s41467-022-34031-z

APA

Madsen, C. T., Refsgaard, J. C., Teufel, F. G., Kjærulff, S. K., Wang, Z., Meng, G., Jessen, C., Heljo, P., Jiang, Q., Zhao, X., Wu, B., Zhou, X., Tang, Y., Jeppesen, J. F., Kelstrup, C. D., Buckley, S. T., Tullin, S., Nygaard-Jensen, J., Chen, X., ... de Lichtenberg, U. (2022). Combining mass spectrometry and machine learning to discover bioactive peptides. Nature Communications, 13, [6235]. https://doi.org/10.1038/s41467-022-34031-z

Vancouver

Madsen CT, Refsgaard JC, Teufel FG, Kjærulff SK, Wang Z, Meng G o.a. Combining mass spectrometry and machine learning to discover bioactive peptides. Nature Communications. 2022;13. 6235. https://doi.org/10.1038/s41467-022-34031-z

Author

Madsen, Christian T. ; Refsgaard, Jan C. ; Teufel, Felix G. ; Kjærulff, Sonny K. ; Wang, Zhe ; Meng, Guangjun ; Jessen, Carsten ; Heljo, Petteri ; Jiang, Qunfeng ; Zhao, Xin ; Wu, Bo ; Zhou, Xueping ; Tang, Yang ; Jeppesen, Jacob F. ; Kelstrup, Christian D. ; Buckley, Stephen T. ; Tullin, Søren ; Nygaard-Jensen, Jan ; Chen, Xiaoli ; Zhang, Fang ; Olsen, Jesper V. ; Han, Dan ; Grønborg, Mads ; de Lichtenberg, Ulrik. / Combining mass spectrometry and machine learning to discover bioactive peptides. I: Nature Communications. 2022 ; Bind 13.

Bibtex

@article{a5e71ec454a94eb197db322bc101d0cb,

title = "Combining mass spectrometry and machine learning to discover bioactive peptides",

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.",

author = "Madsen, {Christian T.} and Refsgaard, {Jan C.} and Teufel, {Felix G.} and Kj{\ae}rulff, {Sonny K.} and Zhe Wang and Guangjun Meng and Carsten Jessen and Petteri Heljo and Qunfeng Jiang and Xin Zhao and Bo Wu and Xueping Zhou and Yang Tang and Jeppesen, {Jacob F.} and Kelstrup, {Christian D.} and Buckley, {Stephen T.} and S{\o}ren Tullin and Jan Nygaard-Jensen and Xiaoli Chen and Fang Zhang and Olsen, {Jesper V.} and Dan Han and Mads Gr{\o}nborg and {de Lichtenberg}, Ulrik",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

doi = "10.1038/s41467-022-34031-z",

language = "English",

volume = "13",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Combining mass spectrometry and machine learning to discover bioactive peptides

AU - Madsen, Christian T.

AU - Refsgaard, Jan C.

AU - Teufel, Felix G.

AU - Kjærulff, Sonny K.

AU - Wang, Zhe

AU - Meng, Guangjun

AU - Jessen, Carsten

AU - Heljo, Petteri

AU - Jiang, Qunfeng

AU - Zhao, Xin

AU - Wu, Bo

AU - Zhou, Xueping

AU - Tang, Yang

AU - Jeppesen, Jacob F.

AU - Kelstrup, Christian D.

AU - Buckley, Stephen T.

AU - Tullin, Søren

AU - Nygaard-Jensen, Jan

AU - Chen, Xiaoli

AU - Zhang, Fang

AU - Olsen, Jesper V.

AU - Han, Dan

AU - Grønborg, Mads

AU - de Lichtenberg, Ulrik

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-022-34031-z

DO - 10.1038/s41467-022-34031-z

M3 - Journal article

C2 - 36266275

AN - SCOPUS:85140232557

VL - 13

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 6235

ER -

ID: 325023683

Biologisk Institut