catabolism of small carbon sources in Paraburkholderia atlantica CCGE1002

GapMind for catabolism of small carbon sources

catabolism of small carbon sources in Paraburkholderia atlantica CCGE1002

Pathways are sorted by name. Sort by completeness instead.

Pathway	Steps
acetate	deh, acs
D-alanine	mctP, dadA
alanine	cycA
arabinose	araF, araG, araH, xacB, xacC, xacD, xacE, xacF
arginine	artJ, artM, artP, artQ, astA, astB, astC, astD, astE
asparagine	ans, aatJ, aatQ, aatM, aatP
aspartate	aatJ, aatQ, aatM, aatP
cellobiose	bgl, gtsA, gtsB, gtsC, gtsD, glk
citrate	citA, acn, icd
citrulline	AO353_03055, AO353_03050, AO353_03045, AO353_03040, arcB, arcC, aruF, aruG, astC, astD, astE
deoxyinosine	nupC, deoD, deoB, deoC, adh, acs
deoxyribonate	deoxyribonate-transport, deoxyribonate-dehyd, ketodeoxyribonate-cleavage, garK, atoA, atoD, atoB
deoxyribose	drdehyd-alpha, drdehyd-beta, drdehyd-cytc, deoxyribonate-transport, deoxyribonate-dehyd, ketodeoxyribonate-cleavage, garK, atoA, atoD, atoB
ethanol	etoh-dh-nad, adh, acs
fructose	frcA, frcB, frcC, scrK
fucose	HSERO_RS05250, HSERO_RS05255, HSERO_RS05260, fucU, fdh, fuconolactonase, fucD, fucDH, KDF-hydrolase
fumarate	dctA
galactose	ytfQ, ytfR, ytfT, yjtF, galdh, galactonolactonase, dgoD, dgoK, dgoA
galacturonate	exuT, udh, uxuL, garD, kdgD, dopDH
gluconate	gntT, gntK, edd, eda
glucose	gtsA, gtsB, gtsC, gtsD, glk
glucose-6-P	uhpT
glucosamine	gamP, nagB
glucuronate	exuT, udh, uxuL, gudD, garL, garR, garK
glutamate	gltI, gltJ, gltK, gltL, gdhA
glycerol	glpF, glpK, glpD, tpi
histidine	PA5503, PA5504, PA5505, hutH, hutU, hutI, hutF, hutG'
isoleucine	livF, livG, livJ, livH, livM, ofo, acdH, ech, ivdG, fadA, prpC, acnD, prpF, acn, prpB
4-hydroxybenzoate	pcaK, pobA, pcaH, pcaG, pcaB, pcaC, pcaD, pcaI, pcaJ, pcaF
D-lactate	mctP, glcD, glcE, glcF
L-lactate	mctP, lldE, lldF, lldG
lactose	lacP, lacZ, galdh, galactonolactonase, dgoD, dgoK, dgoA, glk
leucine	livF, livG, livJ, livH, livM, ilvE, ofo, liuA, liuB, liuD, liuC, liuE, atoA, atoD, atoB
lysine	argT, hisM, hisQ, hisP, cadA, patA, patD, davT, davD, gcdG, gcdH, ech, fadB, atoB
L-malate	dctA
maltose	susB, gtsA, gtsB, gtsC, gtsD, glk
mannitol	mtlE, mtlF, mtlG, mtlK, mt2d, scrK
mannose	frcA, frcB, frcC, man-isomerase, scrK
myoinositol	PGA1_c07300, PGA1_c07310, PGA1_c07320, iolG, iolE, iolD, iolB, iolC, iolJ, mmsA, tpi
NAG	nagF, nagEcb, nagA, nagB
2-oxoglutarate	kgtP
phenylacetate	H281DRAFT_04042, paaK, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
phenylalanine	livF, livG, livH, livM, livJ, ARO8, iorAB, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
proline	HSERO_RS00870, HSERO_RS00885, HSERO_RS00890, HSERO_RS00895, HSERO_RS00900, put1, putA
propionate	mctP, prpE, prpC, acnD, prpF, acn, prpB
putrescine	potA, potB, potC, potD, puuA, puuB, puuC, puuD, gabT, gabD
pyruvate	yjcH, actP
rhamnose	rhaP, rhaQ, rhaS, rhaT', LRA1, LRA2, LRA3, LRA5, LRA6
ribose	rbsA, rbsB, rbsC, rbsK
D-serine	cycA, dsdA
serine	Ac3H11_2396, Ac3H11_1695, Ac3H11_1694, Ac3H11_1693, Ac3H11_1692, sdaB
sorbitol	mtlE, mtlF, mtlG, mtlK, sdh, scrK
succinate	dctA
sucrose	ams, gtsA, gtsB, gtsC, gtsD, glk
threonine	snatA, ltaE, adh, acs, gcvP, gcvT, gcvH, lpd
thymidine	nupG, deoA, deoB, deoC, adh, acs
trehalose	treF, gtsA, gtsB, gtsC, gtsD, glk
tryptophan	aroP, tnaA
tyrosine	Ac3H11_2396, Ac3H11_1695, Ac3H11_1694, Ac3H11_1693, Ac3H11_1692, HPD, hmgA, maiA, fahA, atoA, atoD, atoB
valine	livF, livG, livJ, livH, livM, ofo, acdH, ech, bch, mmsB, mmsA, prpC, acnD, prpF, acn, prpB
xylitol	HSERO_RS17000, HSERO_RS17005, HSERO_RS17010, HSERO_RS17020, xdhA, xylB
xylose	xylF, xylG, xylH, xdh, xylC, xad, kdaD, dopDH

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 Apr 09 2024. 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 2024 update 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