2-deoxy-D-ribose catabolism in Rhizobium grahamii CCGE 502

GapMind for catabolism of small carbon sources

2-deoxy-D-ribose catabolism in Rhizobium grahamii CCGE 502

Best path

Rules

Overview: Deoxyribose utilization in GapMind is based on MetaCyc pathways 2-deoxy-D-ribose degradation I via deoxyribose 5-phosphate aldolase (link) and pathway II via oxidation to 2-deoxy-3-dehydro-D-ribonate (link).

all:

deoxyribose-transport, deoK, deoC and acetaldehyde-degradation
or deoxyribose-dehyd, deoxyribonate-transport and deoxyribonate-degradation
Comment: The deoxyribose 5-phosphate pathway involves the kinase deoK and the aldolase deoC. The oxidative pathway involves oxidation in the periplasm to deoxyribonate.

deoxyribonate-degradation: deoxyribonate-dehyd, ketodeoxyribonate-cleavage, garK and acetoacetate-degradation

Comment: After oxidation of deoxyribonate to 2-deoxy-3-ketoribonate, a cleavage enzyme produces glyceroyl-CoA and acetoacetate; the enzyme for the conversion of glyceroyl-CoA to glycerate is not known; and garK phosphorylates glycerate to 2-phospho-D-glycerate, an intermediate in glycolysis.

acetoacetate-degradation: acetoacetate-activation and atoB

Comment: The acetoacetate is activated to acetoacetyl-CoA, and cleaved by acetyl-CoA acetyltransferase, giving two acetyl-CoA.

acetoacetate-activation:

atoA and atoD
or aacS
Comment: acetyl-CoA:acetoacetyl-CoA transferase (sometimes given EC 2.8.3.9 or EC 2.8.3.8) or succinyl-CoA:acetoacetyl-CoA transferase (EC 2.8.3.5, also known as 3-oxoacid CoA-transferase) can activate acetoacetate. These have an A and B subunit. Alternatively, an ATP-dependent ligase (aacS) can activate acetoacetate (EC 6.2.1.16).

deoxyribose-dehyd: drdehyd-alpha, drdehyd-beta and drdehyd-cytc
acetaldehyde-degradation:

ald-dh-CoA
or adh and acs
or adh, ackA and pta
Comment: Acetaldehyde can be oxidized to acetyl-CoA, or oxidized to acetate and activated to acetyl-CoA by either acetyl-CoA synthetase (acs) or by acetate kinase (ackA) and phosphate acetyltransferase (pta).

deoxyribose-transport: deoP

19 steps (17 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
deoP	deoxyribose transporter	RGCCGE502_RS12300
deoK	deoxyribokinase	RGCCGE502_RS02210
deoC	deoxyribose-5-phosphate aldolase	RGCCGE502_RS01060
adh	acetaldehyde dehydrogenase (not acylating)	RGCCGE502_RS23535	RGCCGE502_RS00735
acs	acetyl-CoA synthetase, AMP-forming	RGCCGE502_RS24205	RGCCGE502_RS24215
Alternative steps:
aacS	acetoacetyl-CoA synthetase	RGCCGE502_RS03670	RGCCGE502_RS02470
ackA	acetate kinase	RGCCGE502_RS11645
ald-dh-CoA	acetaldehyde dehydrogenase, acylating
atoA	acetoacetyl-CoA transferase, A subunit	RGCCGE502_RS33970
atoB	acetyl-CoA C-acetyltransferase	RGCCGE502_RS33960	RGCCGE502_RS23710
atoD	acetoacetyl-CoA transferase, B subunit	RGCCGE502_RS33965
deoxyribonate-dehyd	2-deoxy-D-ribonate 3-dehydrogenase	RGCCGE502_RS31680	RGCCGE502_RS20770
deoxyribonate-transport	2-deoxy-D-ribonate transporter
drdehyd-alpha	2-deoxy-D-ribose dehydrogenase, alpha subunit	RGCCGE502_RS13610	RGCCGE502_RS26530
drdehyd-beta	2-deoxy-D-ribose dehydrogenase, beta subunit	RGCCGE502_RS13605
drdehyd-cytc	2-deoxyribose-D dehydrogenase, cytochrome c component	RGCCGE502_RS25475	RGCCGE502_RS05735
garK	glycerate 2-kinase	RGCCGE502_RS19565	RGCCGE502_RS11730
ketodeoxyribonate-cleavage	2-deoxy-3-keto-D-ribonate cleavage enzyme	RGCCGE502_RS15375
pta	phosphate acetyltransferase	RGCCGE502_RS02115	RGCCGE502_RS10690

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.

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