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 interaction

SUBMITTER: Soleymani Babadi F 

PROVIDER: S-EPMC10374491 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature