L-tyrosine catabolism in Paraburkholderia bryophila 376MFSha3.1

GapMind for catabolism of small carbon sources

L-tyrosine catabolism in Paraburkholderia bryophila 376MFSha3.1

Best path

Ac3H11_2396, Ac3H11_1695, Ac3H11_1694, Ac3H11_1693, Ac3H11_1692, HPD, hmgA, maiA, fahA, atoA, atoD, atoB

Also see fitness data for the top candidates

Rules

Overview: Tyrosine utilization in GapMind is based on MetaCyc pathway tyrosine degradation I, via homogentisate (link). This pathway requires oxygen. Another pathway via 4-hydroxyphenylacetate is known (link), but the 4-hydroxyphenylpyruvate oxidase has not been linked to sequence. The other MetaCyc pathways do not yield fixed carbon or are not reported in prokaryotes.

all: tyrosine-transport and tyrosine-degradation
tyrosine-degradation: HPD, hmgA, maiA, fahA and acetoacetate-degradation

Comment: In pathway I, an aminotransferase (not represented) forms 3-(4-hydroxyphenyl)pyruvate, dioxygenase HPD forms homogentisate, another oxygenase forms 4-maleyl-acetoacetate, an isomerase forms 4-fumaryl-acetoacetate, and a hydrolase yields acetoacetate and fumarate. (Fumarate is part of the TCA cycle so its catabolism is not described.)

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).

tyrosine-transport:

aroP
or Ac3H11_2396, Ac3H11_1695, Ac3H11_1694, Ac3H11_1693 and Ac3H11_1692
or tyrP
or TAT1
or MCT10
or CAT
or tyt1
Comment: Transporters were identified using query: transporter:tyrosine:L-tyrosine:tyr. The ABC transporter livFGHM (with substrate-binding component livK or livJ) may be able to transport tyrosine, but it did not suffice to enable growth of a tyrosine auxotroph (PMC305776), so it is not included.

19 steps (15 with candidates)

Or see definitions of steps

Step	Description	Best candidate	2nd candidate
Ac3H11_2396	L-tyrosine ABC transporter, substrate-binding component component	H281DRAFT_02161	H281DRAFT_04063
Ac3H11_1695	L-tyrosine ABC transporter, permease component 1	H281DRAFT_04062	H281DRAFT_06397
Ac3H11_1694	L-tyrosine ABC transporter, permease component 2	H281DRAFT_04061
Ac3H11_1693	L-tyrosine ABC transporter, ATPase component 1	H281DRAFT_04060	H281DRAFT_02383
Ac3H11_1692	L-tyrosine ABC transporter, ATPase component 2	H281DRAFT_04059	H281DRAFT_02384
HPD	4-hydroxyphenylpyruvate dioxygenase	H281DRAFT_05849	H281DRAFT_03867
hmgA	homogentisate dioxygenase	H281DRAFT_04741
maiA	maleylacetoacetate isomerase	H281DRAFT_01970	H281DRAFT_05672
fahA	fumarylacetoacetate hydrolase	H281DRAFT_04742	H281DRAFT_02070
atoA	acetoacetyl-CoA transferase, A subunit	H281DRAFT_04495	H281DRAFT_01597
atoD	acetoacetyl-CoA transferase, B subunit	H281DRAFT_04496	H281DRAFT_01596
atoB	acetyl-CoA C-acetyltransferase	H281DRAFT_00852	H281DRAFT_00857
Alternative steps:
aacS	acetoacetyl-CoA synthetase	H281DRAFT_02398	H281DRAFT_06226
aroP	L-tyrosine transporter (AroP/FywP)	H281DRAFT_04042	H281DRAFT_01668
CAT	L-tyrosine transporter CAT	H281DRAFT_02180
MCT10	L-tyrosine transporter MCT10
TAT1	L-tyrosine permease TAT1
tyrP	Tyrosine permease
tyt1	L-tyrosine:Na+ symporter Tyt1

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