Digging into the low molecular weight peptidome with the OligoNet web server

Youzhong Liu 1, * Sara Forcisi 2 Marianna Lucio 2 Mourad Harir 2, 3 Florian Bahut 1 Magali Deleris-Bou 4 Sibylle Krieger-Weber 4 Régis D. Gougeon 1 Hervé Alexandre 1 Philippe Schmitt-Kopplin 3, 2, *
* Auteur correspondant
1 PAM - Procédés Alimentaires et Microbiologiques [Dijon]
2 BGC - Research Unit Analytical BioGeoChemistry
3 Chair of Analytical Food Chemistry
4 Lallemand SAS
Abstract : Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as "hidden treasure" of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecular-weight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named "Peptide degradation network" (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns. The ab initio PDN built from yeast metabolic profiling data shows a great similarity with well-known metabolic networks, and could aid biological interpretation. OligoNet allows also an easy evaluation and interpretation of annotated peptides in systems biology, and is freely accessible at https://daniellyz200608105.shinyapps.io/OligoNet/.
Keywords : enzyme-inhibitory peptides mass-spectrometry data bioactive peptides bitter taste metabolomics networks identification prediction quantification oligopeptidase
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Article dans une revue
Scientific Reports, Nature Publishing Group, 2017, 7 (1), 〈10.1038/s41598-017-11786-w〉
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01620015
Contributeur : Pam - Université de Bourgogne <>
Soumis le : vendredi 20 octobre 2017 - 09:23:25
Dernière modification le : mercredi 5 septembre 2018 - 17:04:05

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Youzhong Liu, Sara Forcisi, Marianna Lucio, Mourad Harir, Florian Bahut, et al.. Digging into the low molecular weight peptidome with the OligoNet web server. Scientific Reports, Nature Publishing Group, 2017, 7 (1), 〈10.1038/s41598-017-11786-w〉. 〈hal-01620015〉

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