Evidence map›Paper›PMID 36266275›Full record

ArticleNature communications2022

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 and 14 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Decoding the Heart Failure Peptidome.Circulation. Heart failure · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Christian T MadsenGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark. ctom@novonordisk.com.ORCID 0000-0003-3268-5350
Jan C RefsgaardGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.ORCID 0000-0002-2781-0367
Felix G TeufelGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.ORCID 0000-0003-1275-8065
Sonny K KjærulffGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Zhe WangNovo Nordisk Research Centre China, Beijing, China.
Guangjun MengNovo Nordisk Research Centre China, Beijing, China.
Carsten JessenGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.ORCID 0000-0002-5557-2386
Petteri HeljoGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Qunfeng JiangNovo Nordisk Research Centre China, Beijing, China.
Xin ZhaoNovo Nordisk Research Centre China, Beijing, China.
Bo WuNovo Nordisk Research Centre China, Beijing, China.
Xueping ZhouNovo Nordisk Research Centre China, Beijing, China.ORCID 0000-0002-4506-9138
Yang TangNovo Nordisk Research Centre China, Beijing, China.
Jacob F JeppesenGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Christian D KelstrupGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Stephen T BuckleyGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Søren TullinGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Jan Nygaard-JensenGlobal Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Xiaoli ChenNovo Nordisk Research Centre China, Beijing, China.
Fang ZhangNovo Nordisk Research Centre China, Beijing, China.
Jesper V OlsenDepartment of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-4747-4938
Dan HanNovo Nordisk Research Centre China, Beijing, China.
Mads Grønborg *Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.
Ulrik de Lichtenberg *Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark.ORCID 0000-0002-0985-9343

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine LearningPeptidesAnimalsHumansMammalsMass SpectrometryMicePeptides

Identifiers

PMID36266275
PMCPMC9584923

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.