L-lysine catabolism

GapMind for catabolism of small carbon sources

L-lysine catabolism

Analysis of pathway lysine in 35 genomes

Genome	Best path
Acidovorax sp. GW101-3H11	lysP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Azospirillum brasilense Sp245	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Bacteroides thetaiotaomicron VPI-5482	lysP, lat, amaB, lysN, hglS, ydiJ
Burkholderia phytofirmans PsJN	argT, hisM, hisQ, hisP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Caulobacter crescentus NA1000	lysP, lat, amaB, lysN, hglS, ydiJ
Cupriavidus basilensis 4G11	lysP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Dechlorosoma suillum PS	lysP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Desulfovibrio vulgaris Hildenborough	lysP, lat, amaB, lysN, hglS, ydiJ
Desulfovibrio vulgaris Miyazaki F	lysP, lat, amaB, lysN, hglS, ydiJ
Dinoroseobacter shibae DFL-12	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Dyella japonica UNC79MFTsu3.2	lysP, lat, amaB, lysN, hglS, ydiJ
Echinicola vietnamensis KMM 6221, DSM 17526	lysP, lat, amaB, lysN, hglS, ydiJ
Escherichia coli BW25113	argT, hisM, hisQ, hisP, cadA, patA, patD, davT, davD, glaH, lhgD
Herbaspirillum seropedicae SmR1	lysP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Klebsiella michiganensis M5al	argT, hisM, hisQ, hisP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Magnetospirillum magneticum AMB-1	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Marinobacter adhaerens HP15	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Paraburkholderia bryophila 376MFSha3.1	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pedobacter sp. GW460-11-11-14-LB5	lysP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Phaeobacter inhibens BS107	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas fluorescens FW300-N1B4	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas fluorescens FW300-N2C3	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas fluorescens FW300-N2E2	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas fluorescens FW300-N2E3	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas fluorescens GW456-L13	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas putida KT2440	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas simiae WCS417	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Pseudomonas stutzeri RCH2	argT, hisM, hisQ, hisP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
Shewanella amazonensis SB2B	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Shewanella loihica PV-4	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Shewanella oneidensis MR-1	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Shewanella sp. ANA-3	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Sinorhizobium meliloti 1021	argT, hisM, hisQ, hisP, lysDH, amaB, lysN, hglS, ydiJ
Sphingomonas koreensis DSMZ 15582	lysP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
Synechococcus elongatus PCC 7942	lysP, lat, amaB, lysN, hglS, ydiJ

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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Candidates (tab-delimited)
Steps (tab-delimited)
Rules (tab-delimited)
Protein sequences (fasta format)
Organisms (tab-delimited)
SQLite3 databases

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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 (a fast alternative to protein BLAST) against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer with enzyme models (usually from TIGRFam). Ublast hits may be split across two different proteins.

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

ublast finds a hit to a characterized protein at above 40% identity and 80% coverage, and bits >= other bits+10.
- (Hits to curated proteins without experimental data as to their function are never considered high confidence.)
HMMer finds a hit with 80% coverage of the model, and either other identity < 40 or other coverage < 0.75.

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:

ublast finds a hit at above 40% identity and 70% coverage (ignoring otherBits).
ublast finds a hit at above 30% identity and 80% coverage, and bits >= other bits.
HMMer finds a hit (regardless of coverage or other bits).

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. For diverse bacteria and archaea that can utilize a carbon source, there is a complete high-confidence catabolic pathway (including a transporter) just 38% of the time, and there is a complete medium-confidence pathway 63% of the time. Gaps may be due to:

our ignorance of proteins' functions,
omissions in the gene models,
frame-shift errors in the genome sequence, or
the organism lacks the pathway.

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
the paper from 2022 on GapMind for carbon sources
the source code
instructions for running GapMind on your computer

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

GapMind for catabolism of small carbon sources