Prediction of metabolite–protein interactions based on integration of machine learning and constraint-based modeling

Abstract

Motivation

Metabolite–protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite–protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite–protein interactions using supervised machine learning.

Results

By using gold standards for metabolite–protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite–protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking …

Fatemeh Zare

Associate Professor

My research interests include bioinformatics, computational biology and artificial intelligence.