MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

Moretti, Sébastien; Tran, Van Du T.; Mehl, Florence; Ibberson, Mark; Pagni, Marco

doi:10.1101/2020.09.15.297507

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…The specific architecture of the MLPs is described in Methods. In addition to the Rhea-extracted data, the procedure was repeated for our recently released ECREACT data set (n = 81, 205), which extends the reactions from Rhea with reactions extracted from BRENDA, PathBank, and MetaNetX (12,(45)(46)(47). The accuracies and f-scores of the models are shown in Table 1 together with the training times as well as the training and experimentation energy use.…”

Section: Resultsmentioning

confidence: 99%

Explainable prediction of catalysing enzymes from reactions using multilayer perceptrons

Probst

2023

Preprint

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Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways or the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for arbitrary organic reactions. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.

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Section: Resultsmentioning

confidence: 99%

Explainable prediction of catalysing enzymes from reactions using multilayer perceptrons

Probst

2023

Preprint

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“…To build the consensus models, we followed the pipeline provided in COMMIT (11). Before merging the models obtained from different reconstructions, we unified the reaction and metabolite IDs in the draft models by mapping them to MNXref IDs using the provided MNXref reference files (40). The biomass reaction, if present, and exchange reactions were subsequently removed.…”

Section: Generation Of Draft and Consensus Metabolic Modelsmentioning

confidence: 99%

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Hsieh,

Tandon,

Verbruggen

et al. 2023

Preprint

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Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of metabolic communities.

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“…ModelPolisher permitted the inclusion of annotations in the right fields that can be identified by MEMOTE. Annotation databases that were queried include BiGG (Schellenberger et al, 2010), BioCyc (Karp et al, 2019), CHEBI (Degtyarenko et al, 2008), HMDB (Wishart et al, 2007), Inchikey (Heller et al, 2015), Lipidmaps (Liebisch et al, 2020), KEGG (Kanehisa and Goto, 2000), Reactome (Fabregat et al, 2018), SEED (Seaver et al, 2020), MetaNetX (Moretti et al, 2021), and EC-code, RHEA (Alcántara et al, 2012).…”

Section: Addition Of Annotationsmentioning

confidence: 99%

Whole‐genome sequencing and genome‐scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use

et al. 2022

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Salt tolerant organisms are increasingly being used for the industrial production of high-value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date.Advanced high throughput technologies such as whole-genome sequencing allow in-depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome-scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.

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MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

Cited by 8 publications

References 49 publications

Explainable prediction of catalysing enzymes from reactions using multilayer perceptrons

Explainable prediction of catalysing enzymes from reactions using multilayer perceptrons

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Whole‐genome sequencing and genome‐scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use

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