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).

Packaging during the passaging of viruses in cell cultures yields various phenotypes and is regulated by viral protein expression in infected cells. Although such a packaging mechanism has a profound effect in controlling the virus yield, little is known about the underlying statistical models followed by virus packaging and protein expression among cells infected with the virus. A predictive framework combining identification of the probability density function (PDF) based on log‐likelihood and using the PDF for Monte‐Carlo simulations is developed. The Birnbaum–Saunders distribution was found to be consistent with all three‐virus packaging levels, including nucleocapsids/occlusion‐derived virus (ODV), ODVs/polyhedra, and polyhedra/cell for both wild‐type and genetically modified AcMNPV. Next, it was demonstrated that PDF fitting could be used to compare two viruses having distinctly different genetic configurations. Finally, the identified PDF can be incorporated in RNA synthesis parameters for baculovirus infection to predict the cell‐to‐cell variability in protein expression using Monte‐Carlo simulations. The proposed tool can be used for the estimation of uncertainty in the kinetic parameter and prediction of cell‐to‐cell variability for other biological systems.

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Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs free energy change ( Δ_r G″ °) 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 a higher prediction accuracy as compared to existing GC methods and can capture free energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate the ability of dGPredictor to predict the Gibbs free energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic. This enables performing a thermodynamic analysis for synthetic pathways, thus 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 free 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).