catabolism of small carbon sources in Marivita geojedonensis DPG-138

GapMind for catabolism of small carbon sources

catabolism of small carbon sources in Marivita geojedonensis DPG-138

Pathways are sorted by name. Sort by completeness instead.

Pathway	Steps
acetate	ybhL, acs
D-alanine	Pf6N2E2_5402, Pf6N2E2_5403, Pf6N2E2_5404, Pf6N2E2_5405, dadA
alanine	braC, braD, braE, braF, braG
arabinose	araE, xacB, xacC, xacD, KDG-aldolase, aldA, gyaR, glcB
arginine	artJ, artM, artP, artQ, aruH, aruI, kauB, gbuA, gabT, gabD
asparagine	ans, bztA, bztB, bztC, bztD
aspartate	bztA, bztB, bztC, bztD
cellobiose	bgl, gtsA, gtsB, gtsC, gtsD, glk
citrate	tctA, tctB, tctC, acn, icd
citrulline	AO353_03055, AO353_03050, AO353_03045, AO353_03040, arcB, arcC, ocd, put1, putA
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	fucP, fucU, fucI, fucK, fucA, tpi, aldA
fumarate	Dshi_1194, Dshi_1195
galactose	PfGW456L13_1894, PfGW456L13_1895, PfGW456L13_1896, PfGW456L13_1897, galdh, galactonolactonase, dgoD, dgoK, dgoA
galacturonate	exuT, uxaC, uxaB, uxaA, kdgK, eda
gluconate	gntT, gntK, edd, eda
glucose	gtsA, gtsB, gtsC, gtsD, glk
glucose-6-P	uhpT
glucosamine	gamP, nagB
glucuronate	exuT, udh, gci, garL, garR, garK
glutamate	bztA, bztB, bztC, gltL, gdhA
glycerol	glpF, glpK, glpD, tpi
histidine	hisP, hisM, hisQ, hisJ, hutH, hutU, hutI, hutG
isoleucine	natA, natB, natC, natD, natE, ofo, acdH, ech, ivdG, fadA, pccA, pccB, epi, mcm-large, mcm-small
4-hydroxybenzoate	pcaK, pobA, pcaH, pcaG, pcaB, pcaC, pcaD, pcaI, pcaJ, pcaF
D-lactate	PGA1_c12640, PGA1_c12650, PGA1_c12660, PGA1_c12670, glcD, glcE, glcF
L-lactate	Shew_2731, Shew_2732, L-LDH
lactose	lacP, lacZ, galdh, galactonolactonase, dgoD, dgoK, dgoA, glk
leucine	natA, natB, natC, natD, natE, ilvE, ofo, liuA, liuB, liuD, liuC, liuE, atoA, atoD, atoB
lysine	argT, hisM, hisQ, hisP, davB, davA, davT, davD, gcdG, gcdH, ech, fadB, atoB
L-malate	Dshi_1194, Dshi_1195
maltose	aglE, aglF, aglG, aglK, susB, glk
mannitol	mtlE, mtlF, mtlG, mtlK, mt2d, scrK
mannose	frcA, frcB, frcC, man-isomerase, scrK
myoinositol	PS417_11885, PS417_11890, PS417_11895, iolG, iolE, iolD, iolB, iolC, iolJ, mmsA, tpi
NAG	nagEcba, nagA, nagB
2-oxoglutarate	Psest_0084, Psest_0085
phenylacetate	paaT, paaK, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
phenylalanine	aroP, ARO8, iorAB, paaA, paaB, paaC, paaE, paaG, paaZ1, paaZ2, paaJ1, paaF, paaH, paaJ2
proline	ectP, put1, putA
propionate	putP, prpE, pccA, pccB, epi, mcm-large, mcm-small
putrescine	potA, potB, potC, potD, puuA, puuB, puuC, puuD, gabT, gabD
pyruvate	yjcH, actP
rhamnose	rhaT, LRA1, LRA2, LRA3, LRA4, aldA
ribose	frcA, frcB, frcC, rbsK
D-serine	cycA, dsdA
serine	braC, braD, braE, braF, braG, sdaB
sorbitol	mtlE, mtlF, mtlG, mtlK, sdh, scrK
succinate	Dshi_1194, Dshi_1195
sucrose	aglE, aglF, aglG, aglK, ams, scrK, glk
threonine	braC, braD, braE, braF, braG, ltaE, adh, acs, gcvP, gcvT, gcvH, lpd
thymidine	nupG, deoA, deoB, deoC, adh, acs
trehalose	treF, gtsA, gtsB, gtsC, gtsD, glk
tryptophan	aroP, kynA, kynB, kyn, antA, antB, antC, catA, catB, catC, pcaD, pcaI, pcaJ, pcaF
tyrosine	aroP, HPD, hmgA, maiA, fahA, atoA, atoD, atoB
valine	natA, natB, natC, natD, natE, ofo, acdH, ech, bch, mmsB, mmsA, pccA, pccB, epi, mcm-large, mcm-small
xylitol	PLT5, xdhA, xylB
xylose	gtsA, gtsB, gtsC, gtsD, xylA, xylB

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 24 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