Improving genome-scale metabolic models of incomplete genomes with deep learning
Year: 2024
Type: article
Abstract: Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.
Source: iScience
Institutions: Utrecht University, Royal Netherlands Institute for Sea Research, Wageningen University & Research, Rega Institute for Medical Research, KU Leuven, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, PRocédés biOtechnologiques au Service de l'Environnement, Friedrich Schiller University Jena
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,150
Funders: Alexander von Humboldt-Stiftung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Ministerie van Onderwijs, Cultuur en Wetenschap, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Koninklijk Nederlands Instituut voor Onderzoek der Zee, European Research Council, H2020 European Research Council
OpenAlex ID: W4404148408