Metabolite and reaction inference based on enzyme specificities

Groot, M.J.; Berlo, R. J. P. van; Winden, Wouter A. van; Verheijen, P.J.T.; Reinders, Marcel J. T.; Ridder, Dick de

doi:10.1093/bioinformatics/btp507

Cited by 18 publications

(13 citation statements)

References 35 publications

(42 reference statements)

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“…The focus on endogenously present and organism-specific metabolites has been cited as critical to improving the confidence of compound matches [ 5 ] and thus we complement existing resources that focus on human metabolism. In principle, these predictions could also be made using Reaction Difference Matching (RDM) [ 23 ], machine learning methods [ 31 , 32 ], or other rule-based methods such as ChemAxon’s Metabolizer. Each of these approaches has their benefits; the output really depends on the quality and coverage of the reaction rules used in the analysis.…”

Section: Introductionmentioning

confidence: 99%

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

et al. 2015

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BackgroundIn spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.DescriptionHere we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.ConclusionsMINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0087-1) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

et al. 2015

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“…Here, we propose to extend previous studies of metabolic network evolution (7)(8)(9)(10) by studying the evolutionary aspect of enzyme promiscuity (i.e. of the latent capabilities of enzymes to broaden their specificity to substrates or to process diverse biochemical reactions) (11,12).…”

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confidence: 99%

Origins of Specificity and Promiscuity in Metabolic Networks

Carbonell

Lecointre

Faulon

2011

Journal of Biological Chemistry

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Background: How enzymes evolved to their present form is linked to how extant metabolic pathways emerged. Results: Chemical diversity of reactions parallels enzyme phylogenetic diversity across the tree of life. Conclusion: Enzyme promiscuity plays a prominent role in the evolution of metabolic networks. Significance: Learning about the mechanisms of enzyme evolution might assist us with the identification of primeval catalytic functions and minimal metabolism.

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“…Unlike RDF, which is a fingerprint based representation of differences, RDM (reaction center, difference atom and matched atom) pattern [ 21 ] is a non-fingerprint based representation of transformation region. An extension of the RDM pattern is used in Metabolite and Reaction Inference based on Enzyme Specificities (MaRIboES) [ 22 ] for identifying specificity of an enzyme to catalyze a given metabolite or reaction. SimIndex (SI) and SimZyme [ 5 ] use two dimensional chemical fingerprints for computing chemical similarity for identifying new enzymatic connections in the metabolic networks.…”

Section: Introductionmentioning

confidence: 99%

SimCAL: a flexible tool to compute biochemical reaction similarity

et al. 2018

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BackgroundComputation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation.ResultsTo address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods.ConclusionsSimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2248-5) contains supplementary material, which is available to authorized users.

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Metabolite and reaction inference based on enzyme specificities

Cited by 18 publications

References 35 publications

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

Origins of Specificity and Promiscuity in Metabolic Networks

SimCAL: a flexible tool to compute biochemical reaction similarity

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