catabolism of small carbon sources in Azospirillum brasilense Sp245

GapMind for catabolism of small carbon sources

catabolism of small carbon sources in Azospirillum brasilense Sp245

Pathways are sorted by completeness. Sort by name instead.

Pathway	Steps
xylose	xylF, xylG, xylH, xdh, xylC, xad, DKDP-dehydrog, HDOP-hydrol, gyaR, glcB
proline	AZOBR_RS08235, AZOBR_RS08240, AZOBR_RS08245, AZOBR_RS08250, AZOBR_RS08260, put1, putA
D-alanine	AZOBR_RS08235, AZOBR_RS08240, AZOBR_RS08245, AZOBR_RS08250, AZOBR_RS08260, dadA
cellobiose	bgl, mglA, mglB, mglC, glk
glucose	mglA, mglB, mglC, glk
D-lactate	lctP, glcD, glcE, glcF
L-lactate	lctP, lutA, lutB, lutC
ethanol	etoh-dh-nad, adh, acs
pyruvate	dctM, dctP, dctQ
acetate	actP, acs
2-oxoglutarate	Psest_0084, Psest_0085
fumarate	dctA
L-malate	dctA
succinate	dctA
arginine	braC, braD, braE, braF, braG, arcA, arcB, arcC, rocD, PRO3, put1, putA
galactose	ytfQ, ytfR, ytfT, yjtF, galdh, galactonolactonase, dgoD, dgoK, dgoA
threonine	braC, braD, braE, braF, braG, tdcB, tdcE, pccA, pccB, epi, mcm-large, mcm-small
propionate	lctP, prpE, pccA, pccB, epi, mcm-large, mcm-small
galacturonate	exuT, uxaC, uxaB, uxaA, kdgK, eda
glucuronate	exuT, uxaC, uxuB, uxuA, kdgK, eda
putrescine	potA, potB, potC, potD, patA, patD, gabT, gabD
maltose	susB, mglA, mglB, mglC, glk
sucrose	ams, mglA, mglB, mglC, glk
trehalose	treF, mglA, mglB, mglC, glk
glutamate	braC, braD, braE, braF, braG, gdhA
citrate	tctA, tctB, tctC, acn, icd
asparagine	ans, bztA, bztB, bztC, bztD
aspartate	bztA, bztB, bztC, bztD
alanine	braC, braD, braE, braF, braG
serine	braC, braD, braE, braF, braG, sdaB
mannose	manP, manA
isoleucine	livF, livG, livJ, livH, livM, ofo, acdH, ech, ivdG, fadA, pccA, pccB, epi, mcm-large, mcm-small
valine	livF, livG, livJ, livH, livM, ofo, acdH, ech, bch, mmsB, mmsA, pccA, pccB, epi, mcm-large, mcm-small
leucine	livF, livG, livJ, livH, livM, ilvE, ofo, liuA, liuB, liuD, liuC, liuE, aacS, atoB
lysine	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
histidine	Ac3H11_2562, Ac3H11_2561, Ac3H11_2560, Ac3H11_2555, Ac3H11_2554, hutH, hutU, hutI, hutF, hutG'
glycerol	glpS, glpT, glpP, glpQ, glpV, glpK, glpD, tpi
4-hydroxybenzoate	pcaK, pobA, ligA, ligB, ligC, ligI, ligU, ligJ, ligK
arabinose	gguA, gguB, chvE, xacB, xacC, xacD, KDG-aldolase, aldA, gyaR, glcB
citrulline	AO353_03055, AO353_03050, AO353_03045, AO353_03040, arcB, arcC, rocD, PRO3, put1, putA
lactose	lacP, lacZ, galdh, galactonolactonase, dgoD, dgoK, dgoA, glk
fructose	fruA, fruI, 1pfk, fba, tpi
gluconate	gntA, gntB, gntC, gntK, gnd
glucosamine	SLC2A2, glc-kinase, nagB
NAG	nagEcba, nagA, nagB
glucose-6-P	uhpT
mannitol	PLT5, mt2d, scrK
sorbitol	SOT, sdh, scrK
deoxyinosine	nupC, deoD, deoB, deoC, adh, acs
deoxyribonate	deoxyribonate-transport, deoxyribonate-dehyd, ketodeoxyribonate-cleavage, garK, aacS, atoB
deoxyribose	deoP, deoK, deoC, adh, acs
ribose	rbsU, rbsK
D-serine	cycA, dsdA
tryptophan	aroP, tnaA
xylitol	fruI, x5p-reductase
phenylalanine	livF, livG, livH, livM, livJ, ARO8, ARO10, pad-dh, paaK, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
fucose	fucP, fucU, fdh, fuconolactonase, fucD, fucDH, KDF-hydrolase
thymidine	nupG, deoA, deoB, deoC, adh, acs
rhamnose	rhaT, LRA1, LRA2, LRA3, LRA5, LRA6
myoinositol	iolT, iolG, iolM, iolN, iolO, uxaE, uxuB, uxuA, kdgK, eda
phenylacetate	ppa, paaK, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
tyrosine	Ac3H11_2396, Ac3H11_1695, Ac3H11_1694, Ac3H11_1693, Ac3H11_1692, HPD, hmgA, maiA, fahA, aacS, atoB

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