GapMind for catabolism of small carbon sources

 

L-glutamate catabolism

Analysis of pathway glutamate in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 gltI, gltJ, gltK, gltL, gdhA
Azospirillum brasilense Sp245 braC, braD, braE, braF, braG, gdhA
Bacteroides thetaiotaomicron VPI-5482 gltS, gdhA
Burkholderia phytofirmans PsJN gltI, gltJ, gltK, gltL, gdhA
Caulobacter crescentus NA1000 gltP, gdhA
Cupriavidus basilensis 4G11 gltI, gltJ, gltK, gltL, gdhA
Dechlorosoma suillum PS gltI, gltJ, gltK, gltL, aspA
Desulfovibrio vulgaris Hildenborough dmeA, aspA
Desulfovibrio vulgaris Miyazaki F dmeA, aspA
Dinoroseobacter shibae DFL-12 bztA, bztB, bztC, gltL, gdhA
Dyella japonica UNC79MFTsu3.2 gltP, gdhA
Echinicola vietnamensis KMM 6221, DSM 17526 gltP, gdhA
Escherichia coli BW25113 gltI, gltJ, gltK, gltL, aspA
Herbaspirillum seropedicae SmR1 gltI, gltJ, gltK, gltL, gdhA
Klebsiella michiganensis M5al gltI, gltJ, gltK, gltL, aspA
Magnetospirillum magneticum AMB-1 braC, braD, braE, braF, braG, gdhA
Marinobacter adhaerens HP15 gltP, gdhA
Paraburkholderia bryophila 376MFSha3.1 gltI, gltJ, gltK, gltL, gdhA
Pedobacter sp. GW460-11-11-14-LB5 gltP, gdhA
Phaeobacter inhibens BS107 bztA, bztB, bztC, gltL, gdhA
Pseudomonas fluorescens FW300-N1B4 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas fluorescens FW300-N2C3 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas fluorescens FW300-N2E2 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas fluorescens FW300-N2E3 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas fluorescens GW456-L13 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas putida KT2440 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas simiae WCS417 gltI, gltJ, gltK, gltL, gdhA
Pseudomonas stutzeri RCH2 gltP, gdhA
Shewanella amazonensis SB2B gltP, gdhA
Shewanella loihica PV-4 gltS, gdhA
Shewanella oneidensis MR-1 gltS, gdhA
Shewanella sp. ANA-3 gltS, gdhA
Sinorhizobium meliloti 1021 braC, braD, braE, braF, braG, gdhA
Sphingomonas koreensis DSMZ 15582 gltP, gdhA
Synechococcus elongatus PCC 7942 dmeA, aspA

Confidence: high confidence medium confidence low confidence
transporter – transporters and PTS systems are shaded because predicting their specificity is particularly challenging.

This GapMind analysis is from May 21 2021. The underlying query database was built on May 21 2021.

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About GapMind

Each pathway is defined by a set of rules based on individual steps or genes. Candidates for each step are identified by using ublast against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer. Ublast hits may be split across two different proteins.

A candidate for a step is "high confidence" if either:

where "other" refers to the best ublast hit to a sequence that is not annotated as performing this step (and is not "ignored").

Otherwise, a candidate is "medium confidence" if either:

Other blast hits with at least 50% coverage are "low confidence."

Steps with no high- or medium-confidence candidates may be considered "gaps." For the typical bacterium that can make all 20 amino acids, there are 1-2 gaps in amino acid biosynthesis pathways. Gaps may be due to:

GapMind relies on the predicted proteins in the genome and does not search the six-frame translation. In most cases, you can search the six-frame translation by clicking on links to Curated BLAST for each step definition (in the per-step page).

For more information, see the paper from 2019 on GapMind for amino acid biosynthesis, or view the source code.

If you notice any errors or omissions in the step descriptions, or any questionable results, please let us know

by Morgan Price, Arkin group, Lawrence Berkeley National Laboratory