Teasing out Missing Reactions in Genome-scale Metabolic Networks through Deep Learning

GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps, e.g., reactions may be missing, and unknown gene products may catalyze known reactions. An algorithmic approach to predicting missing reactions in a GEM is a crucial step of gap-filling. Despite existing efforts of predicting missing reactions in GEMs, we still lack a method that is both efficient and accurate. The majority of existing methods require phenotypic or taxonomic information to predict missing reactions. Here we present a deep learning-based method — CHEbyshev Spctral HyperlInk pREdictor (CHESHIRE) — to predict missing reactions of GEMs purely from the metabolic network topology. We demonstrate compelling evidence that CHESHIRE outperforms other topology-based methods in 108 high-quality GEMs. Furthermore, CHESHIRE is able to improve the prediction of fermentation products over 24 draft GEMs reconstructed from an automatic pipeline. Both the internal and external validation results suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.