dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design
Abstract
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 ).
- Publication Date:
- Research Org.:
- Pennsylvania State Univ., University Park, PA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1824882
- Alternate Identifier(s):
- OSTI ID: 1822497; OSTI ID: 1903905
- Grant/Contract Number:
- AC05-00OR22725; 2019897
- Resource Type:
- Published Article
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online) Journal Volume: 17 Journal Issue: 9; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science (PLoS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; metabolites; thermodynamics; enzyme metabolism; enzymes; bioenergetics; metabolic pathways; stereochemistry; neural networks
Citation Formats
Wang, Lin, Upadhyay, Vikas, Maranas, Costas D., and Beard, ed., Daniel A. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. United States: N. p., 2021.
Web. doi:10.1371/journal.pcbi.1009448.
Wang, Lin, Upadhyay, Vikas, Maranas, Costas D., & Beard, ed., Daniel A. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. United States. https://doi.org/10.1371/journal.pcbi.1009448
Wang, Lin, Upadhyay, Vikas, Maranas, Costas D., and Beard, ed., Daniel A. Mon .
"dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design". United States. https://doi.org/10.1371/journal.pcbi.1009448.
@article{osti_1824882,
title = {dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design},
author = {Wang, Lin and Upadhyay, Vikas and Maranas, Costas D. and Beard, ed., Daniel A.},
abstractNote = {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 ).},
doi = {10.1371/journal.pcbi.1009448},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 17,
place = {United States},
year = {Mon Sep 27 00:00:00 EDT 2021},
month = {Mon Sep 27 00:00:00 EDT 2021}
}
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