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

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. Author summary: An organism's growth and survival are largely guided by its ability to synthesize crucial metabolites like amino acids and ribonucleotides from such compounds as water, glucose and nitrogenous molecules like ammonia. Accurate knowledge of such biochemical reactions—catalyzed by enzymes encoded within the genome—can advance our understanding of disease drivers as well as guide attempts at engineering strains of bacteria with desired metabolic capacities. While biochemical experiments can accurately characterize metabolism, these are time-consuming at genome-scale. Instead, genome-scale metabolic models can be computationally built then iteratively refined through comparisons of in silico simulation results and biochemical experiments. Here, we describe Architect, a method for automatic enzyme annotation and metabolic model reconstruction. Our tool leverages the strengths of existing enzyme annotation tools to predict the biochemical capacities of an organism and then uses these predictions to build a simulation-ready metabolic model. We find that Architect produces more accurate enzyme annotations than the individual tools, as well as higher-quality metabolic models compared to other automatic metabolic model reconstruction tools. We provide Architect to the metabolic modelling community in the hope that it may facilitate the transition from knowing an organism's encoded sequences to an understanding of its metabolic capacities. [ABSTRACT FROM AUTHOR]