GapMind for catabolism of small carbon sources

 

D-sorbitol (glucitol) catabolism

Analysis of pathway sorbitol in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Azospirillum brasilense Sp245 SOT, sdh, scrK
Bacteroides thetaiotaomicron VPI-5482 SOT, sdh, scrK
Burkholderia phytofirmans PsJN mtlE, mtlF, mtlG, mtlK, sdh, scrK
Caulobacter crescentus NA1000 SOT, sdh, scrK
Cupriavidus basilensis 4G11 SOT, sdh, scrK
Dechlorosoma suillum PS SOT, sdh, scrK
Desulfovibrio vulgaris Hildenborough mtlA, srlD
Desulfovibrio vulgaris Miyazaki F mtlA, srlD
Dinoroseobacter shibae DFL-12 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Dyella japonica UNC79MFTsu3.2 SOT, sdh, scrK
Echinicola vietnamensis KMM 6221, DSM 17526 SOT, sdh, scrK
Escherichia coli BW25113 srlA, srlB, srlE, srlD
Herbaspirillum seropedicae SmR1 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Klebsiella michiganensis M5al srlA, srlB, srlE, srlD
Magnetospirillum magneticum AMB-1 SOT, sdh, scrK
Marinobacter adhaerens HP15 mtlA, srlD
Paraburkholderia bryophila 376MFSha3.1 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pedobacter sp. GW460-11-11-14-LB5 mtlA, srlD
Phaeobacter inhibens BS107 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas fluorescens FW300-N1B4 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas fluorescens FW300-N2C3 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas fluorescens FW300-N2E2 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas fluorescens FW300-N2E3 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas fluorescens GW456-L13 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas putida KT2440 mtlA, srlD
Pseudomonas simiae WCS417 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Pseudomonas stutzeri RCH2 mtlA, srlD
Shewanella amazonensis SB2B SOT, sdh, scrK
Shewanella loihica PV-4 mtlA, srlD
Shewanella oneidensis MR-1 SOT, sdh, scrK
Shewanella sp. ANA-3 SOT, sdh, scrK
Sinorhizobium meliloti 1021 mtlE, mtlF, mtlG, mtlK, sdh, scrK
Sphingomonas koreensis DSMZ 15582 SOT, sdh, scrK
Synechococcus elongatus PCC 7942 SOT, sdh, scrK

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 Sep 17 2021. The underlying query database was built on Sep 17 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