Improving genome-scale metabolic models of incomplete genomes with deep learning

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.

Authors

Institutions

Publication Details

Journal
iScience
Published
2024-11-07
DOI
https://doi.org/10.1016/j.isci.2024.111349
Citations
8
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
1.87

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving genome-scale metabolic models of incomplete genomes with deep learning

Haris Zafeiropoulos, Daniel Garza, Meine D. Boer, Chrats Melkonian et al.
8 citations
iScience
Microbial Metabolic Engineering and Bioproduction
1.87
article

Improving genome-scale metabolic models of incomplete genomes with deep learning

Haris Zafeiropoulos, Daniel Garza, Meine D. Boer, Chrats Melkonian, Bas E. Dutilh, Andreas F. Haas
article en
8 citations

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.

iScienceVol. 27(12)
Utrecht University (NL), Rega Institute for Medical Research (BE), Université Paris-Saclay (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Royal Netherlands Institute for Sea Research (NL), Friedrich Schiller University Jena (DE), Wageningen University & Research (NL), KU Leuven (BE)
Alexander von Humboldt-Stiftung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Koninklijk Nederlands Instituut voor Onderzoek der Zee, H2020 European Research Council
Openalex Percentile: Top 14%
Microbial Metabolic Engineering and Bioproduction
1.87
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.