fumarate catabolism in Hoeflea phototrophica DFL-43

GapMind for catabolism of small carbon sources

fumarate catabolism in Hoeflea phototrophica DFL-43

Best path

Dshi_1194, Dshi_1195

Rules

Overview: Since fumarate is a TCA cycle intermediate, GapMind represent its uptake only.

all: fumarate-transport
fumarate-transport:

dctA
or sdcL
or dauA
or dctM, dctP and dctQ
or SLC26dg
or Dshi_1194 and Dshi_1195
Comment: Transporters were identified using query: transporter:fumarate. Fumarate:succinate antiporters or fumarate:proton antiporters (probably for efflux) were not included. Subunits of succinate dehdydrogenase/fumarate reductase were also excluded.

9 steps (5 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
Dshi_1194	TRAP transporter for succinate, fumarate, L-malate, and 2-oxoglutarate, fused 4TM/12TM components	HPDFL43_RS17245	HPDFL43_RS15840
Dshi_1195	TRAP transporter for succinate, fumarate, L-malate, and 2-oxoglutarate, substrate-binding component	HPDFL43_RS17240
Alternative steps:
dauA	fumarate transporter DauA
dctA	fumarate:H+ symporter DctA
dctM	fumarate TRAP transporter, large permease component DctM	HPDFL43_RS19725	HPDFL43_RS17285
dctP	fumarate TRAP transporter, substrate-binding component DctP	HPDFL43_RS19060
dctQ	fumarate TRAP transporter, small permease component DctQ
sdcL	fumarate:Na+ symporter SdcL
SLC26dg	fumarate transporter SLC26dg	HPDFL43_RS13995

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