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

Abstract

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.

The commensal protistTritrichomonas musculusexhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota

“…Functional annotation including Gene Ontology (GO) term and PFAM prediction was performed with InterProScan v.5.30-69.0 95 and the hmmscan algorithm, from HmmerWeb version 2.41.2 96 , with minimum 1e-05 E-value cut-offs. Enzyme Commission (EC) numbers were assigned to proteins using the enzyme prediction program ARCHITECT 97 with minimum 0.5 confidence scores. Genes encoding adhesins, meiosis and cell cycle-related proteins were identified based on protein-level homology with relevant T. vaginalis proteins 31, 44 as follows.…”

“…Annotation was carried out with the Maker v2.31 pipeline 24 using SNAP v.2013-11-29 25 and Augustus v.3.3.1 26 for gene model prediction. Functional annotation was performed with InterProScan v.5.30-69.0 27 , HmmerWeb v.2.41.2 28 and Architect 29 . Genes encoding adhesins, meiosis and cell cycle-related proteins were identified based on sequence homology with T. vaginalis proteins retrieved from the TrichDB database 30–32 .…”

An automated model annotation system (AMAS) for SBML models

“…For example, , a web resource for genome-scale reconstruction and analysis, obtains the annotation of assembled genome sequence using , an automated annotation service for archaeal and bacterial genomes ( Aziz et al 2008 , Henry et al 2010 , Mendoza et al 2019 ). Other examples include ( Dias et al 2015 , Mendoza et al 2019 ) and ( Nursimulu et al 2022 ).…”