skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: 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 ( 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 ).  more » « less
Award ID(s):
2019897
PAR ID:
10380140
Author(s) / Creator(s):
; ;
Editor(s):
Beard, Daniel A.
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
17
Issue:
9
ISSN:
1553-7358
Page Range / eLocation ID:
e1009448
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Frontiers in Bioinformatics)
    We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams. 
    more » « less
  2. ; (, Bioinformatics)
    Abstract MotivationA factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation. MethodsWe introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories. ResultsThis optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases. Availability and implementationSource code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu. 
    more » « less
  3. ; ; ; ; ; (, npj Systems Biology and Applications)
    null (Ed.)
    Abstract Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth. 
    more » « less
  4. ; (, Proceedings of the 28th Conference on Research in Computational Molecular Biology (RECOMB), Springer LNCS 14758)
    Ma, J (Ed.)
    Perhaps the most fundamental model in synthetic and sys- tems biology for inferring pathways in metabolic reaction networks is a metabolic factory: a system of reactions that starts from a set of source compounds and produces a set of target molecules, while conserving or not depleting intermediate metabolites. Finding a shortest factory—that minimizes a sum of real-valued weights on its reactions to infer the most likely pathway—is NP-complete. The current state-of-the-art for shortest factories solves a mixed-integer linear program with a major drawback: it requires the user to set a critical parameter, where too large a value can make optimal solutions infeasible, while too small a value can yield degenerate solutions due to numerical error. We present the first robust algorithm for optimal factories that is both parameter-free (relieving the user from determining a parameter setting) and degeneracy-free (guaranteeing it finds an optimal nondegen- erate solution). We also give for the first time a complete characterization of the graph-theoretic structure of shortest factories via cuts of hyper- graphs that reveals two important classes of degenerate solutions which were overlooked and potentially output by the prior state-of-the-art. In addition we settle the relationship between the two established pathway models of hyperpaths and factories by proving that hyperpaths are actu- ally a subclass of factories. Comprehensive experiments over all instances from the standard metabolic reaction databases in the literature demon- strate our algorithm is fast in practice, quickly finding optimal factories in large real-world networks containing thousands of reactions. A preliminary implementation of our algorithm for robust optimal factories in a new tool called Freeia is available free for research use at http://freeia.cs.arizona.edu. 
    more » « less
  5. ; ; ; ; (, Methods in molecular biology)
    Lee, YJ (Ed.)
    The ability to study and visualize metabolites on a cellular and sub-cellular level is important for gaining insights into biological pathways and metabolism of multicellular organisms. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a powerful analytical tool for metabolomics experiments due to its high sensitivity and small sampling size. The spatial resolution in MALDI-MSI is mainly limited by the number of molecules available in a small sampling size. When the sampling size is low enough to achieve cellular or subcellular spatial resolution, signal intensity is sacrificed making poorly ionized metabolites difficult to detect. To overcome this limitation, on-tissue chemical derivatization reactions have been used to enhance the desorption/ionization efficiency of selected classes of compounds by adding a functional group with a permanent positive charge or one that can be easily ionized. By utilizing several chemical derivatizations in parallel, metabolite coverage can be drastically improved. This chapter outlines methodology for sample preparation and data analysis for on-tissue chemical derivatization using various derivatization reagents. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email