myo-inositol catabolism in Caulobacter crescentus NA1000

GapMind for catabolism of small carbon sources

myo-inositol catabolism in Caulobacter crescentus NA1000

Best path

iatP, iatA, ibpA, iolG, iolE, iolD, iolB, iolC, iolJ, mmsA, tpi

Also see fitness data for the top candidates

Rules

Overview: Myo-inositol degradation in GapMind is based on MetaCyc pathways myo-inositol degradation I via inosose dehydratase (link) and pathway II inosose dehydrogenase (link).

all:

myo-inositol-transport, iolG, iolE, iolD, iolB, iolC, iolJ, mmsA and tpi
or myo-inositol-transport, iolG, iolM, iolN, iolO, uxaE, uxuB, uxuA, kdgK and eda
Comment: Both pathways begin with the 2-dehydrogenase (iolG) forming scyllo-inosose. In pathway I, inosose dehydratase (iolE) forms 3D-(3,5/4)-trihydroxycyclohexane-1,2-dione, followed by a ring-cleaving hydrolase to 5-deoxy-D-glucuronate, isomerization to 5-dehydro-2-deoxy-D-gluconate, phosphorylation, and cleavage by an aldolase to 3-oxopropionate (malonate semialdehyde) and glycerone phosphate; the 3-oxopropionate is oxidized to acetyl-CoA while the glycerone phosphate is converted by triose-phosphate isomerase to glyceraldehyde 3-phosphate. In pathway II, a dehydrogenase forms 3-dehydro scyllo-inosose (also known as 2,4-diketo-inositol), a hydratase forms 5-dehydro-L-gluconate, an epimerase forms D-tagaturonate, another forms to D-fructonate, a reductase forms D-mannonate, a dehydratase forms 2-keto-3-deoxygluconate, a kinase forms 2-keto-3-deoxy-gluconate 6-phosphate, and an aldolase forms glyceraldehyde-3-phosphate and pyruvate.

myo-inositol-transport:

PGA1_c07300, PGA1_c07310 and PGA1_c07320
or iatP, iatA and ibpA
or PS417_11885, PS417_11890 and PS417_11895
or iolT
or SMIT1
or HMIT
or iolF
Comment: Transporters were identified using: query: transporter:myo-inositol:myoinositol:m-inositol:inositol

29 steps (21 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
iatP	myo-inositol ABC transporter, permease component IatP	CCNA_00904
iatA	myo-inositol ABC transporter, ATPase component IatA	CCNA_00903
ibpA	myo-inositol ABC transporter, substrate-binding component IbpA	CCNA_00902
iolG	myo-inositol 2-dehydrogenase	CCNA_01354	CCNA_02094
iolE	scyllo-inosose 2-dehydratase	CCNA_01358
iolD	3D-(3,5/4)-trihydroxycyclohexane-1,2-dione hydrolase	CCNA_01357
iolB	5-deoxy-D-glucuronate isomerase	CCNA_01359
iolC	5-dehydro-2-deoxy-D-gluconate kinase	CCNA_01356
iolJ	5-dehydro-2-deoxyphosphogluconate aldolase	CCNA_01356	CCNA_03360
mmsA	malonate-semialdehyde dehydrogenase	CCNA_01360	CCNA_02357
tpi	triose-phosphate isomerase	CCNA_01970	CCNA_03359
Alternative steps:
eda	2-keto-3-deoxygluconate 6-phosphate aldolase	CCNA_01562	CCNA_00825
HMIT	myo-inositol:H+ symporter	CCNA_00857
iolF	myo-inositol:H+ symporter
iolM	2-inosose 4-dehydrogenase
iolN	2,4-diketo-inositol hydratase
iolO	5-dehydro-L-gluconate epimerase
iolT	myo-inositol:H+ symporter	CCNA_00857
kdgK	2-keto-3-deoxygluconate kinase	CCNA_01110	CCNA_01563
PGA1_c07300	myo-inositol ABC transport, substrate-binding component
PGA1_c07310	myo-inositol ABC transporter, permease component	CCNA_00904
PGA1_c07320	myo-inositol ABC transporter, ATPase component	CCNA_01670	CCNA_03235
PS417_11885	myo-inositol ABC transporter, substrate-binding component
PS417_11890	myo-inositol ABC transporter, ATPase component	CCNA_00903
PS417_11895	myo-inositol ABC transporter, permease component	CCNA_00904
SMIT1	myo-inositol:Na+ symporter
uxaE	D-tagaturonate epimerase
uxuA	D-mannonate dehydratase	CCNA_02903	CCNA_00566
uxuB	D-mannonate dehydrogenase	CCNA_01554

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.

Downloads

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

Related tools

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