Accurate Atom-Mapping Computation for Biochemical Reactions

Latendresse, Mario; Malerich, Jeremiah P.; Travers, Michael; Karp, Peter D.

doi:10.1021/ci3002217

Cited by 73 publications

(105 citation statements)

References 16 publications

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“…Six academic and commercially available atom mapping algorithms were included in our evaluation Reaction Decoder Tool (RDT, [10]), Determination of Reaction Mechanisms (DREAM, [11]), AutoMapper 5.0.1 (AutoMapper, [12], ChemAxon, Budapest, Hungary), Canonical Labeling for Clique Approximation (CLCA, [13]), Minimum Weighted Edit-Distance (MWED, [14]) within Pathway Tools, and InfoChem-Map (ICMAP, [15], InfoChem, Munich, Germany). These algorithms implement different prediction strategies or they use molecular properties, such as the bonds with hydrogen atoms or the use of the stereochemistry to predict atom mapping.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

et al. 2017

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The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0223-1) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

et al. 2017

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“…As a result, atom mappers can give optimal and suboptimal solutions that must be manually confirmed to ensure their accuracy. Most importantly, previous work related to atom mappers has focused on how to efficiently compute metrics for chemical structures, but the accuracy of these methods has not been assessed for large networks [1, 3]. This last point is an important issue because methods devoted to pathway discovery have used the results of atom mapping and reactant pairings as input to define new pathways.…”

Section: Objectivementioning

confidence: 99%

“…In the manuscript, we did not show the entire list of TS pairs or CTSs yielded by the method [3].

A statistical precision value could not be generated for 49 CTSs because they had fewer than 10 elements (CTSs from 23 to 71).

The reactions and TS pairs that do not have a concordant pair in the RCLASS need manual curation.

In contrast to the RPAIR data set, our method does not allow us to pair a compound more than one time with another for the same reaction.

…”

Section: Limitationsmentioning

confidence: 99%

Reactant pairs and reaction organization patterns produced by a new rule-based approach

Vazquez-Hernandez¹,

Loza²,

Gutiérrez-Ríos³

2018

BMC Res Notes

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ObjectivesImprovements in bioinformatics applications for the enzyme identification of biochemical reactions, enzyme classifications, mining for specific inhibitors and pathfinding require the accurate computational detection of reaction similarity. We provide a set of substrate-product pairs, clustered by reactions that share similar chemical transformation patterns, for which accuracy was calculated, comparing this set with manually curated data sets.Data descriptionThe data were analyzed by a new method that naturally split each reaction into compound pairs and loner compounds, which we called architectures (Vazquez-Hernandez et al. in BMC Syst Biol 12:63, 2018). The data include a set of 7491 curated reactions from the KEGG-Ligand data set. The data are presented in two formats, a string format and a tree structure, both of which reflect the splitting process and the final architectures of each reaction. We are also reporting sets of reactions that show similar splitting patterns naturally grouped into clusters of tree structures. The compound pairs in each cluster were compared with the reactant pairs proposed by the KEGG-RCLASS data set, and a match precision value is also provided. These data were collected with the aim of providing research with a confident set of reactant pairs that is useful for selecting between alternative substrate-product pairs predicted by pathfinders.

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“…These models contain the mapping of each reaction, which consists in a bijection of the reactants' to the products' carbon atoms [19], elucidating how the labeling patterns in the carbon skeleton are propagated through all the metabolites in the network.…”

Section: B Carbon Atoms Mappingmentioning

confidence: 99%

Algorithms to infer metabolic flux ratios from fluxomics data

Carreira¹,

Rocha²,

Villas‐Bôas

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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Abstract-In silico cell simulation approaches based in the use of genome-scale metabolic models (GSMMs) and constraintbased methods such as Flux Balance Analysis are gaining importance, but methods to integrate these approaches with omics data are still greatly needed. In this work, the focus relies on fluxomics data that provide valuable information on the intracellular fluxes, although in many cases in an indirect, incomplete and noisy way. The proposed framework enables the integration of fluxomics data, in the form of 13 C labeling distribution for metabolite fragments, with GSMMs enriched with carbon atom transition maps. The algorithms implemented allow to infer labeling distributions for fragments/metabolites not measured and to build expressions for the relevant flux ratios that can be then used to enrich constraint-based methods for flux determination. This approach does not require any assumptions on the metabolic network and reaction reversibility, allowing to compute ratios originating from coupled joint points of the network. Also, when enough data do not exist, the system tries to infer ratio bounds from the measurements.

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Accurate Atom-Mapping Computation for Biochemical Reactions

Cited by 73 publications

References 16 publications

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

Reactant pairs and reaction organization patterns produced by a new rule-based approach

Algorithms to infer metabolic flux ratios from fluxomics data

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