Data from: Modeling central metabolism and energy biosynthesis across microbial life

Background: Automatically generated bacterial metabolic models, and even
some curated models, lack accuracy in predicting energy yields due to poor
representation of key pathways in energy biosynthesis and the electron
transport chain (ETC). Further compounding the problem, complex
interlinking pathways in genome-scale metabolic models, and the need for
extensive gapfilling to support complex biomass reactions, often results
in predicting unrealistic yields or unrealistic physiological flux
profiles Results: To overcome this challenge, we developed methods and
tools (http://coremodels.mcs.anl.gov) to build high quality core metabolic
models (CMM) representing accurate energy biosynthesis based on a well
studied, phylogenetically diverse set of model organisms. We compare these
models to explore the variability of core pathways across all microbial
life, and by analyzing the ability of our core models to synthesize ATP
and essential biomass precursors, we evaluate the extent to which the core
metabolic pathways and functional ETCs are known for all microbes. 6,600
(80%) of our models were found to have some type of aerobic ETC, whereas
5,100 (62%) have an anaerobic ETC, and 1,279 (15%) do not have any ETC.
Using our manually curated ETC and energy biosynthesis pathways with no
gapfilling at all, we predict accurate ATP yields for nearly 5586 (70%) of
the models under aerobic and anaerobic growth conditions. This study
revealed gaps in our knowledge of the central pathways that result in
2,495 (30%) CMMs being unable to produce ATP under any of the tested
conditions. We then established a methodology for the systematic
identification and correction of inconsistent annotations using core
metabolic models coupled with phylogenetic analysis. Conclusions: We
predict accurate energy yields based on our improved annotations in energy
biosynthesis pathways and the implementation of diverse ETC reactions
across the microbial tree of life. We highlighted missing annotations that
were essential to energy biosynthesis in our models. We examine the
diversity of these pathways across all microbial life and enable the
scientific community to explore the analyses generated from this
large-scale analysis of over 8000 microbial genomes.