GapMind for catabolism of small carbon sources

 

L-asparagine catabolism

Analysis of pathway asparagine in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 ans, aatJ, aatQ, aatM, aatP
Azospirillum brasilense Sp245 ans, bztA, bztB, bztC, bztD
Bacteroides thetaiotaomicron VPI-5482 yhiT, ans
Burkholderia phytofirmans PsJN ans, aatJ, aatQ, aatM, aatP
Caulobacter crescentus NA1000 ans, glt
Cupriavidus basilensis 4G11 ans, aatJ, aatQ, aatM, aatP
Dechlorosoma suillum PS ans, aatJ, aatQ, aatM, aatP
Desulfovibrio vulgaris Hildenborough ans, dauA
Desulfovibrio vulgaris Miyazaki F ans, dauA
Dinoroseobacter shibae DFL-12 ans, bztA, bztB, bztC, bztD
Dyella japonica UNC79MFTsu3.2 ans, BPHYT_RS17540
Echinicola vietnamensis KMM 6221, DSM 17526 ans, glt
Escherichia coli BW25113 ans, aatJ, aatQ, aatM, aatP
Herbaspirillum seropedicae SmR1 ans, aatJ, aatQ, aatM, aatP
Klebsiella michiganensis M5al ans, aatJ, aatQ, aatM, aatP
Magnetospirillum magneticum AMB-1 ans, dauA
Marinobacter adhaerens HP15 ans, glt
Paraburkholderia bryophila 376MFSha3.1 ans, aatJ, aatQ, aatM, aatP
Pedobacter sp. GW460-11-11-14-LB5 ans, glt
Phaeobacter inhibens BS107 ans, bztA, bztB, bztC, bztD
Pseudomonas fluorescens FW300-N1B4 ans, aatJ, aatQ, aatM, aatP
Pseudomonas fluorescens FW300-N2C3 ans, aatJ, aatQ, aatM, aatP
Pseudomonas fluorescens FW300-N2E2 ans, aatJ, aatQ, aatM, aatP
Pseudomonas fluorescens FW300-N2E3 ans, aatJ, aatQ, aatM, aatP
Pseudomonas fluorescens GW456-L13 ans, aatJ, aatQ, aatM, aatP
Pseudomonas putida KT2440 ans, aatJ, aatQ, aatM, aatP
Pseudomonas simiae WCS417 ans, aatJ, aatQ, aatM, aatP
Pseudomonas stutzeri RCH2 ans, glt
Shewanella amazonensis SB2B ans, glt
Shewanella loihica PV-4 ans, glt
Shewanella oneidensis MR-1 ans, glt
Shewanella sp. ANA-3 ans, glt
Sinorhizobium meliloti 1021 ans, aapJ, aapQ, aapM, aapP
Sphingomonas koreensis DSMZ 15582 ans, glt
Synechococcus elongatus PCC 7942 ans, natF, bgtB', natH, bgtA

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