A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions

Area of Science:

• Systems Biology
• Metabolic Engineering
• Bioinformatics

Background:

• Advances in medicine and biotechnology require a comprehensive understanding of biological processes.
• Omics data is abundant, yet significant knowledge gaps persist in metabolic pathway annotations.
• Current methods for gap filling in metabolic models are limited to known reactions.

Purpose of the Study:

• To introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a novel workflow.
• To identify and curate nonannotated metabolic functions within genomes.
• To enhance genome-scale metabolic models (GEMs) and improve their predictive capabilities.

Main Methods:

• NICEgame utilizes the ATLAS of Biochemistry and existing GEMs to identify metabolic gaps.
• The workflow proposes alternative known and hypothetical reactions to fill identified gaps.
• Thermodynamic feasibility is assessed, and candidate genes are suggested for catalyzing reactions.

Main Results:

• NICEgame was applied to the iML1515 GEM of *Escherichia coli*.
• Metabolic gaps were identified and addressed, enhancing the *E. coli* genome annotation.
• 47% of identified metabolic gaps in the *E. coli* GEM were successfully resolved.

Conclusions:

• NICEgame effectively identifies and curates nonannotated metabolic functions in genomes.
• The workflow significantly enhances genome-scale metabolic models by resolving metabolic gaps.
• NICEgame, built on open-source software, is applicable to any GEM, advancing metabolic predictions for biotechnology and medicine.