GapMind for catabolism of small carbon sources

 

D-lactate catabolism

Analysis of pathway D-lactate in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 lctP, glcD, glcE, glcF
Azospirillum brasilense Sp245 lctP, glcD, glcE, glcF
Bacteroides thetaiotaomicron VPI-5482 lctP, D-LDH
Burkholderia phytofirmans PsJN mctP, glcD, glcE, glcF
Caulobacter crescentus NA1000 lctP, D-LDH
Cupriavidus basilensis 4G11 lctP, glcD, glcE, glcF
Dechlorosoma suillum PS lctP, glcD, glcE, glcF
Desulfovibrio vulgaris Hildenborough lctP, D-LDH
Desulfovibrio vulgaris Miyazaki F lctP, D-LDH
Dinoroseobacter shibae DFL-12 PGA1_c12640, PGA1_c12650, PGA1_c12660, PGA1_c12670, glcD, glcE, glcF
Dyella japonica UNC79MFTsu3.2 lctP, D-LDH
Echinicola vietnamensis KMM 6221, DSM 17526 larD, D-LDH
Escherichia coli BW25113 lctP, glcD, glcE, glcF
Herbaspirillum seropedicae SmR1 lctP, glcD, glcE, glcF
Klebsiella michiganensis M5al lctP, D-LDH
Magnetospirillum magneticum AMB-1 lctP, D-LDH
Marinobacter adhaerens HP15 lctP, glcD, glcE, glcF
Paraburkholderia bryophila 376MFSha3.1 mctP, glcD, glcE, glcF
Pedobacter sp. GW460-11-11-14-LB5 lctP, D-LDH
Phaeobacter inhibens BS107 PGA1_c12640, PGA1_c12650, PGA1_c12660, PGA1_c12670, glcD, glcE, glcF
Pseudomonas fluorescens FW300-N1B4 lctP, glcD, glcE, glcF
Pseudomonas fluorescens FW300-N2C3 lctP, glcD, glcE, glcF
Pseudomonas fluorescens FW300-N2E2 lctP, glcD, glcE, glcF
Pseudomonas fluorescens FW300-N2E3 lctP, glcD, glcE, glcF
Pseudomonas fluorescens GW456-L13 lctP, D-LDH
Pseudomonas putida KT2440 lctP, glcD, glcE, glcF
Pseudomonas simiae WCS417 lctP, D-LDH
Pseudomonas stutzeri RCH2 lctP, glcD, glcE, glcF
Shewanella amazonensis SB2B lctP, D-LDH
Shewanella loihica PV-4 lctP, D-LDH
Shewanella oneidensis MR-1 lctP, D-LDH
Shewanella sp. ANA-3 lctP, D-LDH
Sinorhizobium meliloti 1021 lctP, glcD, glcE, glcF
Sphingomonas koreensis DSMZ 15582 lctP, glcD, glcE, glcF
Synechococcus elongatus PCC 7942 lctP, D-LDH

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