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

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated...

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Autores principales:	Lin Wang, Vikas Upadhyay, Costas D Maranas
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/56f09c81520b41be9a31771d1666e17a
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spelling	oai:doaj.org-article:56f09c81520b41be9a31771d1666e17a2021-12-02T19:58:13ZdGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.1553-734X1553-735810.1371/journal.pcbi.1009448https://doaj.org/article/56f09c81520b41be9a31771d1666e17a2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009448https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'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).Lin WangVikas UpadhyayCostas D MaranasPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009448 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lin Wang Vikas Upadhyay Costas D Maranas dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
description	Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'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).
format	article
author	Lin Wang Vikas Upadhyay Costas D Maranas
author_facet	Lin Wang Vikas Upadhyay Costas D Maranas
author_sort	Lin Wang
title	dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
title_short	dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
title_full	dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
title_fullStr	dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
title_full_unstemmed	dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
title_sort	dgpredictor: automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/56f09c81520b41be9a31771d1666e17a
work_keys_str_mv	AT linwang dgpredictorautomatedfragmentationmethodformetabolicreactionfreeenergypredictionanddenovopathwaydesign AT vikasupadhyay dgpredictorautomatedfragmentationmethodformetabolicreactionfreeenergypredictionanddenovopathwaydesign AT costasdmaranas dgpredictorautomatedfragmentationmethodformetabolicreactionfreeenergypredictionanddenovopathwaydesign
_version_	1718375778261925888

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

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