4.6 Article

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

Journal

PLOS COMPUTATIONAL BIOLOGY
Volume 17, Issue 9, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1009448

Keywords

-

Funding

  1. Center for Bioenergy Innovation (CBI), a U.S. Department of Energy Bioenergy Research Center
  2. Office of Biological and Environmental Research in the DOE Office of Science [DE-AC05-00OR22725]
  3. National Science Foundation - National AI Research Institutes Program of the Directorate for Computer and Information Science and Engineering (CISE) [2019897]
  4. Division of Chemistry (CHE)
  5. Division of Chemical, Bioengineering, and Environmental Transport Systems (CBET)

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dGPredictor is an automated molecular fingerprint-based thermodynamic analysis tool that considers stereochemistry within metabolite structures, increasing reaction coverage with accuracy comparable to existing GC methods. It captures Gibbs energy changes for a wide range of reactions and can predict the energy change for novel reactions, seamlessly integrating within metabolic pathway design tools. A graphical user interface allows easy access for predicting standard Gibbs energy changes under various conditions.
Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (Delta,G'degrees) 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 InChl strings (https://github.com/maranasgroup/dGPredictor).

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