GapMind: Automated annotation of amino acid biosynthesis

Price, Morgan N.; Deutschbauer, Adam M.; Arkin, Adam P.

doi:10.1101/741918

Cited by 2 publications

(1 citation statement)

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“…Extensive curation was then performed by evaluating metabolic pathways guided by the literature and the KEGG database. Pathways for catabolism of small carbon sources in particular were reconstructed based on predictions obtained from GapMind (86). Gene-protein-reaction associations were curated based on published gene essentiality data for growth in minimal (30) and complete (13) media as well as enzyme commission (EC) number predictions obtained from DeepEC (31).…”

Section: Methodsmentioning

confidence: 99%

Genome-scale metabolic modelling of lifestyle changes in Rhizobium leguminosarum

Schulte

Ramachandran

Papachristodoulou

et al. 2021

Preprint

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Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses.

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Section: Methodsmentioning

confidence: 99%

Genome-scale metabolic modelling of lifestyle changes in Rhizobium leguminosarum

Schulte

Ramachandran

Papachristodoulou

et al. 2021

Preprint

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GapMind: Automated Annotation of Amino Acid Biosynthesis

2020

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GapMind is a Web-based tool for annotating amino acid biosynthesis in bacteria and archaea (http://papers.genomics.lbl.gov/gaps). GapMind incorporates many variant pathways and 130 different reactions, and it analyzes a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins. GapMind correctly handles fusion proteins and split proteins, which often cause errors for best-hit approaches. To improve GapMind’s coverage, we examined genetic data from 35 bacteria that grow in defined media without amino acids, and we filled many gaps in amino acid biosynthesis pathways. For example, we identified additional genes for arginine synthesis with succinylated intermediates in Bacteroides thetaiotaomicron, and we propose that Dyella japonica synthesizes tyrosine from phenylalanine. Nevertheless, for many bacteria and archaea that grow in minimal media, genes for some steps still cannot be identified. To help interpret potential gaps, GapMind checks if they match known gaps in related microbes that can grow in minimal media. GapMind should aid the identification of microbial growth requirements. IMPORTANCE Many microbes can make all of the amino acids (the building blocks of proteins). In principle, we should be able to predict which amino acids a microbe can make, and which it requires as nutrients, by checking its genome sequence for all of the necessary genes. However, in practice, it is difficult to check for all of the alternative pathways. Furthermore, new pathways and enzymes are still being discovered. We built an automated tool, GapMind, to annotate amino acid biosynthesis in bacterial and archaeal genomes. We used GapMind to list gaps: cases where a microbe makes an amino acid but a complete pathway cannot be identified in its genome. We used these gaps, together with data from mutants, to identify new pathways and enzymes. However, for most bacteria and archaea, we still do not know how they can make all of the amino acids.

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GapMind: Automated annotation of amino acid biosynthesis

Abstract: GapMind is a web-based tool for annotating amino acid biosynthesis in bacteria and archaea

Cited by 2 publications

References 62 publications

Genome-scale metabolic modelling of lifestyle changes in Rhizobium leguminosarum

Genome-scale metabolic modelling of lifestyle changes in Rhizobium leguminosarum

GapMind: Automated Annotation of Amino Acid Biosynthesis

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