Prediction and integration of metabolite-protein interactions with genome-scale metabolic models.

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications. • We provided a classification of constraint-based modeling approaches for prediction MPIs and integration of their effects in large-scale models of metabolism. • We compared the performance of four approaches for prediction of MPIs using GEMs of E. coli and S. cerevisiae , and identified that SIMMER and SCOUR showed the largest macro-averaged F1-score on S. cerevisiae and E. coli , respectively. • Approaches that rely on structural features and easy-to-obtain metabolic phenotypes resulted in more accurate predictions of MPIs, providing the basis of future developments approaches for integrating the effects of MPIs in genome-scale metabolic models. [ABSTRACT FROM AUTHOR]