Motivation: Metabolic networks are complex systems of chemical reactions proceeding via physical interactions between metabolites and proteins. We aimed to predict previously unknown compound-protein interactions (CPI) in metabolic networks by applying biclique extension, a network-structure-based prediction method.
Results: We developed a workflow, named BiPredict, to predict CPIs based on biclique extension and applied it to Escherichia coli and human using their respective known CPI networks as input. Depending on the chosen biclique size and using a STITCH-derived E.coli CPI network as input, a sensitivity of 39% and an associated precision of 59% was reached. For the larger human STITCH network, a sensitivity of 78% with a false-positive rate of <5% and precision of 75% was obtained. High performance was also achieved when using KEGG metabolic-reaction networks as input. Prediction performance significantly exceeded that of randomized controls and compared favorably to state-of-the-art deep-learning methods. Regarding metabolic process involvement, TCA-cycle and ribosomal processes were found enriched among predicted interactions. BiPredict can be used for network curation, may help increase the efficiency of experimental testing of CPIs, and can readily be applied to other species.
Availability and implementation: BiPredict and related datasets are available at https://github.com/SandraThieme/BiPredict.
Supplementary information: Supplementary data are available at Bioinformatics Advances online.
© The Author(s) 2022. Published by Oxford University Press.