4.6 Article

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 18, 期 9, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1010452

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资金

  1. Canadian Institute for Health Research grant [PJT-152921]
  2. Natural Sciences and Engineering Research Council [RGPIN-201906852]
  3. SickKids RestraComp scholarship
  4. Canada Foundation for Innovation under Compute Canada
  5. Government of Ontario
  6. Ontario Research Fund-Research Excellence
  7. University of Toronto

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Constraint-based modeling is a powerful framework for studying cellular metabolism. In this paper, the authors introduce Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. Their method shows improved precision and recall compared to existing tools, and the resulting metabolic model is of high quality. Given the importance of metabolic models in various applications, this work contributes to the field and provides a valuable tool for researchers.
Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

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