Combination of network and molecule structure accurately predicts competitive inhibitory interactions

• Published in: Computational and structural biotechnology journal Vol. 19; pp. 2170 - 2178
• Main Authors: Razaghi-Moghadam, Zahra, Sokolowska, Ewelina M., Sowa, Marcin A., Skirycz, Aleksandra, Nikoloski, Zoran
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Genome-scale metabolic models; Metabolite-protein interactions; Supervised machine learning; Genome-scale metabolic models; Supervised machine learning; Metabolite-protein interactions
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2021.04.012

[Display omitted] 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 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 metabolite-protein 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.