Alignments for a candidate for pcaF in Dinoroseobacter shibae DFL-12

GapMind for catabolism of small carbon sources

Alignments for a candidate for pcaF in Dinoroseobacter shibae DFL-12

Align subunit of β-ketoadipyl CoA thiolase (EC 2.3.1.174; EC 2.3.1.16) (characterized)
to candidate 3610436 Dshi_3817 beta-ketoadipyl CoA thiolase (RefSeq)

Query= metacyc::MONOMER-3207
         (400 letters)



>FitnessBrowser__Dino:3610436
          Length = 400

 Score =  543 bits (1400), Expect = e-159
 Identities = 272/399 (68%), Positives = 321/399 (80%), Gaps = 1/399 (0%)

Query: 3   DVFICDAIRTPIGRFGGALAGVRADDLAAVPLKALIEPNPAVQWDQVDEVFFGCANQAGE 62
           D  ICD +RTPIGR+GGAL+ VRADDLAA+P+ AL+  NP V W +VDEV +G ANQAGE
Sbjct: 2   DAVICDGVRTPIGRYGGALSSVRADDLAALPIAALMARNPGVDWARVDEVIYGAANQAGE 61

Query: 63  DNRNVARMALLLAGLPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESMSR 122
           DNRNVARMA LLAGLPE +PG+T+NRLCASGMDA+G A R I +GE +LAIAGG+ESMSR
Sbjct: 62  DNRNVARMAALLAGLPEEVPGLTVNRLCASGMDAVGAAARGIKAGEYDLAIAGGIESMSR 121

Query: 123 APFVMGKAESGYSRNMKLEDTTIGWRFINPLMKSQYGVDSMPETADNVADDYQVSRADQD 182
           APFVM KAES ++R   + DTTIGWRF+NP + + +G+D+MP+TAD VA  Y++SRADQD
Sbjct: 122 APFVMPKAESAFTRAATVHDTTIGWRFVNPKIAAMHGIDTMPQTADTVAAAYEISRADQD 181

Query: 183 AFALRSQQKAAAAQAAGFFAEEIVPVRIAHK-KGETIVERDEHLRPETTLEALTKLKPVN 241
           AFA RSQ + AAA AAG FA+EIVPV +  +     +V+RDEH RP T    L  LK +N
Sbjct: 182 AFAARSQARWAAADAAGLFADEIVPVPVPQRGSAPILVDRDEHPRPGTDAARLAGLKGIN 241

Query: 242 GPDKTVTAGNASGVNDGAAALILASAEAVKKHGLTPRARVLGMASGGVAPRVMGIGPVPA 301
           GP  +VTAGNASGVNDGAAAL++ASA A + HGLTP ARV+GMAS GVAPRVMGIGPVPA
Sbjct: 242 GPGLSVTAGNASGVNDGAAALLIASAAAARAHGLTPMARVVGMASAGVAPRVMGIGPVPA 301

Query: 302 VRKLTERLGVAVSDFDVIELNEAFASQGLAVLRELGVADDAPQVNPNGGAIALGHPLGMS 361
            RKL +R G+ +   DVIELNEAFASQ LA LR+LG+ADD  +VNPNGGAIA+GHPLGMS
Sbjct: 302 SRKLLDRAGLTLDQMDVIELNEAFASQSLATLRQLGLADDDVRVNPNGGAIAMGHPLGMS 361

Query: 362 GARLVLTALHQLEKSGGRKGLATMCVGVGQGLALAIERV 400
           GARLVLTA HQL ++GGR  L TMCVGVGQG AL +ERV
Sbjct: 362 GARLVLTAAHQLRRTGGRYALCTMCVGVGQGTALILERV 400


Lambda     K      H
   0.318    0.134    0.383 

Gapped
Lambda     K      H
   0.267   0.0410    0.140 


Matrix: BLOSUM62
Gap Penalties: Existence: 11, Extension: 1
Number of Sequences: 1
Number of Hits to DB: 524
Number of extensions: 15
Number of successful extensions: 2
Number of sequences better than 1.0e-02: 1
Number of HSP's gapped: 1
Number of HSP's successfully gapped: 1
Length of query: 400
Length of database: 400
Length adjustment: 31
Effective length of query: 369
Effective length of database: 369
Effective search space:   136161
Effective search space used:   136161
Neighboring words threshold: 11
Window for multiple hits: 40
X1: 16 ( 7.3 bits)
X2: 38 (14.6 bits)
X3: 64 (24.7 bits)
S1: 41 (21.7 bits)
S2: 50 (23.9 bits)

Align candidate 3610436 Dshi_3817 (beta-ketoadipyl CoA thiolase (RefSeq))
to HMM TIGR02430 (pcaF: 3-oxoadipyl-CoA thiolase (EC 2.3.1.174))

# hmmsearch :: search profile(s) against a sequence database
# HMMER 3.3.1 (Jul 2020); http://hmmer.org/
# Copyright (C) 2020 Howard Hughes Medical Institute.
# Freely distributed under the BSD open source license.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# query HMM file:                  ../tmp/path.carbon/TIGR02430.hmm
# target sequence database:        /tmp/gapView.19488.genome.faa
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Query:       TIGR02430  [M=400]
Accession:   TIGR02430
Description: pcaF: 3-oxoadipyl-CoA thiolase
Scores for complete sequences (score includes all domains):
   --- full sequence ---   --- best 1 domain ---    -#dom-
    E-value  score  bias    E-value  score  bias    exp  N  Sequence                         Description
    ------- ------ -----    ------- ------ -----   ---- --  --------                         -----------
   2.1e-207  674.9  15.8   2.3e-207  674.7  15.8    1.0  1  lcl|FitnessBrowser__Dino:3610436  Dshi_3817 beta-ketoadipyl CoA th


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__Dino:3610436  Dshi_3817 beta-ketoadipyl CoA thiolase (RefSeq)
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  674.7  15.8  2.3e-207  2.3e-207       2     400 .]       2     400 .]       1     400 [] 1.00

  Alignments for each domain:
  == domain 1  score: 674.7 bits;  conditional E-value: 2.3e-207
                         TIGR02430   2 evyivdairtpiGrygGslssvraddlaavplkallarnpsldaaaiddvilGcanqaGednrnvarmaallaGlpv 78 
                                       ++ i+d++rtpiGrygG+lssvraddlaa+p++al+arnp +d+a +d+vi+G+anqaGednrnvarmaallaGlp+
  lcl|FitnessBrowser__Dino:3610436   2 DAVICDGVRTPIGRYGGALSSVRADDLAALPIAALMARNPGVDWARVDEVIYGAANQAGEDNRNVARMAALLAGLPE 78 
                                       578************************************************************************** PP

                         TIGR02430  79 svpgttvnrlcgsgldalglaaraikaGeadlviaGGvesmsrapfvlGkadsafsrsakledttiGwrfvnpklka 155
                                       +vpg tvnrlc+sg+da+g aar+ikaGe dl iaGG+esmsrapfv+ ka+saf+r a ++dttiGwrfvnpk++a
  lcl|FitnessBrowser__Dino:3610436  79 EVPGLTVNRLCASGMDAVGAAARGIKAGEYDLAIAGGIESMSRAPFVMPKAESAFTRAATVHDTTIGWRFVNPKIAA 155
                                       ***************************************************************************** PP

                         TIGR02430 156 lyGvdsmpetaenvaeefgvsredqdafalrsqqrtaaaqakGffaeeivpveikqkkGeetvvdkdehlraettle 232
                                       ++G+d+mp+ta+ va  +++sr+dqdafa rsq+r+aaa+a+G+fa+eivpv ++q+   +  vd+deh+r+ t   
  lcl|FitnessBrowser__Dino:3610436 156 MHGIDTMPQTADTVAAAYEISRADQDAFAARSQARWAAADAAGLFADEIVPVPVPQRGSAPILVDRDEHPRPGTDAA 232
                                       ***************************************************************************** PP

                         TIGR02430 233 alaklkavvredgtvtaGnasGvndGaaalllaseeavkrhgltprarilaaasaGveprvmGlgpvpavkkllara 309
                                       +la lk+++ ++ +vtaGnasGvndGaaall+as+ a+++hgltp ar++++asaGv+prvmG+gpvpa +kll+ra
  lcl|FitnessBrowser__Dino:3610436 233 RLAGLKGINGPGLSVTAGNASGVNDGAAALLIASAAAARAHGLTPMARVVGMASAGVAPRVMGIGPVPASRKLLDRA 309
                                       ***************************************************************************** PP

                         TIGR02430 310 glsledldvielneafaaqalavlrelgladddarvnpnGGaialGhplGasGarlvltalkqleksggryalatlc 386
                                       gl+l+++dvielneafa+q+la+lr+lgladdd rvnpnGGaia+GhplG+sGarlvlta +ql+++ggryal+t+c
  lcl|FitnessBrowser__Dino:3610436 310 GLTLDQMDVIELNEAFASQSLATLRQLGLADDDVRVNPNGGAIAMGHPLGMSGARLVLTAAHQLRRTGGRYALCTMC 386
                                       ***************************************************************************** PP

                         TIGR02430 387 iGvGqGialvierv 400
                                       +GvGqG al++erv
  lcl|FitnessBrowser__Dino:3610436 387 VGVGQGTALILERV 400
                                       *************8 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (400 nodes)
Target sequences:                          1  (400 residues searched)
Passed MSV filter:                         1  (1); expected 0.0 (0.02)
Passed bias filter:                        1  (1); expected 0.0 (0.02)
Passed Vit filter:                         1  (1); expected 0.0 (0.001)
Passed Fwd filter:                         1  (1); expected 0.0 (1e-05)
Initial search space (Z):                  1  [actual number of targets]
Domain search space  (domZ):               1  [number of targets reported over threshold]
# CPU time: 0.01u 0.00s 00:00:00.01 Elapsed: 00:00:00.01
# Mc/sec: 11.97
//
[ok]

This GapMind analysis is from Sep 17 2021. The underlying query database was built on Sep 17 2021.

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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