dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Wang, Lin; Upadhyay, Vikas; Maranas, Costas D.; Beard, ed., Daniel A.

doi:10.1371/journal.pcbi.1009448

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Journal Article · Mon Sep 27 04:00:00 EDT 2021 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1009448· OSTI ID:1824882

Wang, Lin; ; ; Beard, ed., Daniel A.

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change ( Δ _r G ′ ^o ) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings ( https://github.com/maranasgroup/dGPredictor ).

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1824882

Alternate ID(s):: OSTI ID: 1822497
OSTI ID: 1903905

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 9 Vol. 17; ISSN 1553-7358

Publisher:: Public Library of Science (PLoS)Copyright Statement

Country of Publication:: United States

Language:: English

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