Combination of network and molecule structure accurately predicts competitive inhibitory interactions
- Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitiveMining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metaboliteprotein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.…
Author details: | Zahra Razaghi-MoghadamORCiD, Ewelina SokolowskaORCiD, Marcin A. Sowa, Aleksandra SkiryczORCiDGND, Zoran NikoloskiORCiDGND |
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DOI: | https://doi.org/10.1016/j.csbj.2021.04.012 |
ISSN: | 2001-0370 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/34136091 |
Title of parent work (English): | Computational and structural biotechnology journal |
Publisher: | Research Network of Computational and Structural Biotechnology (RNCSB) |
Place of publishing: | Gotenburg |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/04/22 |
Publication year: | 2021 |
Release date: | 2024/09/20 |
Tag: | Genome-scale metabolic models; Metabolite-protein interactions; Supervised machine learning |
Volume: | 19 |
Number of pages: | 9 |
First page: | 2170 |
Last Page: | 2178 |
Funding institution: | Max Planck SocietyMax Planck SocietyFoundation CELLEX; European UnionEuropean Commission [862201] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
DOAJ gelistet | |
License (German): | CC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International |