Aligments for a candidate for paaJ2 in Caulobacter crescentus NA1000

GapMind for catabolism of small carbon sources

Aligments for a candidate for paaJ2 in Caulobacter crescentus NA1000

Align Beta-ketoadipyl-CoA thiolase; 3-oxoadipyl-CoA thiolase; EC 2.3.1.174 (characterized)
to candidate CCNA_02490 CCNA_02490 3-ketoacyl-CoA thiolase

Query= SwissProt::Q8VPF1
         (401 letters)



>FitnessBrowser__Caulo:CCNA_02490
          Length = 399

 Score =  567 bits (1462), Expect = e-166
 Identities = 289/396 (72%), Positives = 331/396 (83%), Gaps = 1/396 (0%)

Query: 6   YICDAVRTPIGRFGGSLAAVRADDLAAVPVKALVERNPQVDWSQLDEVYLGCANQAGEDN 65
           YICD +RTPIGR+GGSL+ VRADDLAA+P+KALV RNP +D + +DE+ LG ANQAGEDN
Sbjct: 5   YICDGIRTPIGRYGGSLSKVRADDLAAIPLKALVARNPSLDLAAIDEIVLGSANQAGEDN 64

Query: 66  RNVARMALLLAGLPDSVPGVTLNRLCASGMDAVGTAFRAIASGEAELVIAGGVESMSRAP 125
           RNVARMALLLAG P SVPGVT+NRLCASG++AVG A RAIASG  +LVIAGGVESMSRAP
Sbjct: 65  RNVARMALLLAGYPVSVPGVTVNRLCASGLEAVGYAARAIASGHNDLVIAGGVESMSRAP 124

Query: 126 YVMGKADSAFGRGQKIEDTTIGWRFINPLMKAQYGVDAMPETADNVADDYKVSRADQDAF 185
           +VMGKADSAF R  +I DTTIGWRF+NP M+  YGVD+MPETA+NVA DY V+R DQDAF
Sbjct: 125 FVMGKADSAFSRSAEIFDTTIGWRFVNPAMRKLYGVDSMPETAENVATDYGVNREDQDAF 184

Query: 186 ALRSQQLAGRAQAAGYFAEEIVPVVIKGKKGETVVDADEHLRPDTTLEALAKLKPVNGPD 245
           ALRSQ     AQA G+ A EI PV I GK G T+VD DEH R +TT+EALAKLKP+    
Sbjct: 185 ALRSQARTAAAQANGFLAGEITPVEIPGKAGPTIVDRDEHPR-ETTMEALAKLKPIVREG 243

Query: 246 KTVTAGNASGVNDGSVALILASAEAVKKHGLKARAKVLGMASAGVAPRVMGIGPVPAVRK 305
            TVTAGNASGVNDG+VAL++AS +AVK+HGL  RA++ G ASAGV PRVMGIGPVPAVRK
Sbjct: 244 GTVTAGNASGVNDGAVALVIASEDAVKRHGLTPRARITGYASAGVEPRVMGIGPVPAVRK 303

Query: 306 LLERLNLSVADFDVIELNEAFAAQGLAVTRELGIADDDARVNPNGGAIALGHPLGASGAR 365
           L+ +  L++ DFDV+ELNEAFAAQGLAV R+LG+ DD A VN NGGAIALGHPLGASGAR
Sbjct: 304 LMAKTGLAIGDFDVVELNEAFAAQGLAVLRQLGLPDDGAHVNANGGAIALGHPLGASGAR 363

Query: 366 LVLTAVHQLEKSGGQRGLCTMCVGVGQGVALAVERV 401
           LVLTA+ QLE SGGQRGL T+C+GVGQG ALA ERV
Sbjct: 364 LVLTALRQLEASGGQRGLATLCIGVGQGAALAFERV 399


Lambda     K      H
   0.317    0.134    0.379 

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: 559
Number of extensions: 23
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: 401
Length of database: 399
Length adjustment: 31
Effective length of query: 370
Effective length of database: 368
Effective search space:   136160
Effective search space used:   136160
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 (21.6 bits)
S2: 50 (23.9 bits)

Align candidate CCNA_02490 CCNA_02490 (3-ketoacyl-CoA thiolase)
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.4687.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
    ------- ------ -----    ------- ------ -----   ---- --  --------                             -----------
   7.3e-227  738.9  11.6   8.2e-227  738.7  11.6    1.0  1  lcl|FitnessBrowser__Caulo:CCNA_02490  CCNA_02490 3-ketoacyl-CoA thiola


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__Caulo:CCNA_02490  CCNA_02490 3-ketoacyl-CoA thiolase
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  738.7  11.6  8.2e-227  8.2e-227       2     400 .]       3     399 .]       2     399 .] 1.00

  Alignments for each domain:
  == domain 1  score: 738.7 bits;  conditional E-value: 8.2e-227
                             TIGR02430   2 evyivdairtpiGrygGslssvraddlaavplkallarnpsldaaaiddvilGcanqaGednrnvarmaalla 74 
                                           ++yi+d+irtpiGrygGsls+vraddlaa+plkal+arnpsld+aaid+++lG+anqaGednrnvarma+lla
  lcl|FitnessBrowser__Caulo:CCNA_02490   3 AAYICDGIRTPIGRYGGSLSKVRADDLAAIPLKALVARNPSLDLAAIDEIVLGSANQAGEDNRNVARMALLLA 75 
                                           69*********************************************************************** PP

                             TIGR02430  75 GlpvsvpgttvnrlcgsgldalglaaraikaGeadlviaGGvesmsrapfvlGkadsafsrsakledttiGwr 147
                                           G+pvsvpg+tvnrlc+sgl+a+g+aarai++G++dlviaGGvesmsrapfv+Gkadsafsrsa+++dttiGwr
  lcl|FitnessBrowser__Caulo:CCNA_02490  76 GYPVSVPGVTVNRLCASGLEAVGYAARAIASGHNDLVIAGGVESMSRAPFVMGKADSAFSRSAEIFDTTIGWR 148
                                           ************************************************************************* PP

                             TIGR02430 148 fvnpklkalyGvdsmpetaenvaeefgvsredqdafalrsqqrtaaaqakGffaeeivpveikqkkGeetvvd 220
                                           fvnp++++lyGvdsmpetaenva+++gv+redqdafalrsq+rtaaaqa+Gf+a ei+pvei++k G +t+vd
  lcl|FitnessBrowser__Caulo:CCNA_02490 149 FVNPAMRKLYGVDSMPETAENVATDYGVNREDQDAFALRSQARTAAAQANGFLAGEITPVEIPGKAG-PTIVD 220
                                           *******************************************************************.9**** PP

                             TIGR02430 221 kdehlraettlealaklkavvredgtvtaGnasGvndGaaalllaseeavkrhgltprarilaaasaGveprv 293
                                           +deh+r ett+ealaklk++vre+gtvtaGnasGvndGa+al++ase+avkrhgltprari+++asaGveprv
  lcl|FitnessBrowser__Caulo:CCNA_02490 221 RDEHPR-ETTMEALAKLKPIVREGGTVTAGNASGVNDGAVALVIASEDAVKRHGLTPRARITGYASAGVEPRV 292
                                           ******.****************************************************************** PP

                             TIGR02430 294 mGlgpvpavkkllaraglsledldvielneafaaqalavlrelgladddarvnpnGGaialGhplGasGarlv 366
                                           mG+gpvpav+kl+a++gl++ d+dv+elneafaaq+lavlr+lgl+dd+a+vn+nGGaialGhplGasGarlv
  lcl|FitnessBrowser__Caulo:CCNA_02490 293 MGIGPVPAVRKLMAKTGLAIGDFDVVELNEAFAAQGLAVLRQLGLPDDGAHVNANGGAIALGHPLGASGARLV 365
                                           ************************************************************************* PP

                             TIGR02430 367 ltalkqleksggryalatlciGvGqGialvierv 400
                                           ltal+qle+sgg+++latlciGvGqG al++erv
  lcl|FitnessBrowser__Caulo:CCNA_02490 366 LTALRQLEASGGQRGLATLCIGVGQGAALAFERV 399
                                           *********************************8 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (400 nodes)
Target sequences:                          1  (399 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 0.01s 00:00:00.02 Elapsed: 00:00:00.02
# Mc/sec: 7.53
//
[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, or view the source code.

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