Alignments for a candidate for paaJ2 in Pseudomonas simiae WCS417

GapMind for catabolism of small carbon sources

Alignments for a candidate for paaJ2 in Pseudomonas simiae WCS417

Align subunit of β-ketoadipyl CoA thiolase (EC 2.3.1.174; EC 2.3.1.16) (characterized)
to candidate GFF1320 PS417_06710 beta-ketoadipyl CoA thiolase

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



>FitnessBrowser__WCS417:GFF1320
          Length = 400

 Score =  717 bits (1852), Expect = 0.0
 Identities = 363/400 (90%), Positives = 383/400 (95%)

Query: 1   MRDVFICDAIRTPIGRFGGALAGVRADDLAAVPLKALIEPNPAVQWDQVDEVFFGCANQA 60
           MRDVFICDAIRTPIGRFGG L+ VRADDLAA+P+KALIE NP+V W  VDEVF GCANQA
Sbjct: 1   MRDVFICDAIRTPIGRFGGGLSTVRADDLAALPIKALIERNPSVDWTAVDEVFLGCANQA 60

Query: 61  GEDNRNVARMALLLAGLPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESM 120
           GEDNRNVARMALLLAGLPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESM
Sbjct: 61  GEDNRNVARMALLLAGLPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESM 120

Query: 121 SRAPFVMGKAESGYSRNMKLEDTTIGWRFINPLMKSQYGVDSMPETADNVADDYQVSRAD 180
           SRAPFVMGKA++ +SR MKLEDTTIGWRFINPLMK+QYGVD+MP+TADNVADDY VSRAD
Sbjct: 121 SRAPFVMGKADAAFSRTMKLEDTTIGWRFINPLMKAQYGVDAMPQTADNVADDYHVSRAD 180

Query: 181 QDAFALRSQQKAAAAQAAGFFAEEIVPVRIAHKKGETIVERDEHLRPETTLEALTKLKPV 240
           QDAFALRSQQ+ AAAQAAGFFAEEIVPVR+AHKKGET+VE+DEH R +TTLEALTKLKPV
Sbjct: 181 QDAFALRSQQRTAAAQAAGFFAEEIVPVRVAHKKGETVVEQDEHPRADTTLEALTKLKPV 240

Query: 241 NGPDKTVTAGNASGVNDGAAALILASAEAVKKHGLTPRARVLGMASGGVAPRVMGIGPVP 300
           NGPDKTVTAGNASGVNDGAAALILASAEAVKKHGLT RARVLGMAS GVAPRVMGIGPVP
Sbjct: 241 NGPDKTVTAGNASGVNDGAAALILASAEAVKKHGLTARARVLGMASAGVAPRVMGIGPVP 300

Query: 301 AVRKLTERLGVAVSDFDVIELNEAFASQGLAVLRELGVADDAPQVNPNGGAIALGHPLGM 360
           AVRKL ERLG+AV+DFDVIELNEAFASQGLAVLRELG+ADDAPQVNPNGGAIALGHPLGM
Sbjct: 301 AVRKLVERLGLAVTDFDVIELNEAFASQGLAVLRELGIADDAPQVNPNGGAIALGHPLGM 360

Query: 361 SGARLVLTALHQLEKSGGRKGLATMCVGVGQGLALAIERV 400
           SGARLVLTALHQLEK+GG KGLATMCVGVGQGLALAIER+
Sbjct: 361 SGARLVLTALHQLEKTGGSKGLATMCVGVGQGLALAIERI 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: 655
Number of extensions: 16
Number of successful extensions: 1
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 GFF1320 PS417_06710 (beta-ketoadipyl 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.28628.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
    ------- ------ -----    ------- ------ -----   ---- --  --------                           -----------
   1.8e-226  737.6  10.8   2.1e-226  737.4  10.8    1.0  1  lcl|FitnessBrowser__WCS417:GFF1320  PS417_06710 beta-ketoadipyl CoA 


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__WCS417:GFF1320  PS417_06710 beta-ketoadipyl CoA thiolase
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  737.4  10.8  2.1e-226  2.1e-226       1     400 []       2     400 .]       2     400 .] 1.00

  Alignments for each domain:
  == domain 1  score: 737.4 bits;  conditional E-value: 2.1e-226
                           TIGR02430   1 kevyivdairtpiGrygGslssvraddlaavplkallarnpsldaaaiddvilGcanqaGednrnvarmaallaG 75 
                                         ++v+i+dairtpiGr+gG+ls+vraddlaa+p+kal++rnps+d++a+d+v+lGcanqaGednrnvarma+llaG
  lcl|FitnessBrowser__WCS417:GFF1320   2 RDVFICDAIRTPIGRFGGGLSTVRADDLAALPIKALIERNPSVDWTAVDEVFLGCANQAGEDNRNVARMALLLAG 76 
                                         689************************************************************************ PP

                           TIGR02430  76 lpvsvpgttvnrlcgsgldalglaaraikaGeadlviaGGvesmsrapfvlGkadsafsrsakledttiGwrfvn 150
                                         lp+s+pg+t+nrlc+sg+da+g+a+rai++Ge +l iaGGvesmsrapfv+Gkad+afsr+ kledttiGwrf+n
  lcl|FitnessBrowser__WCS417:GFF1320  77 LPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESMSRAPFVMGKADAAFSRTMKLEDTTIGWRFIN 151
                                         *************************************************************************** PP

                           TIGR02430 151 pklkalyGvdsmpetaenvaeefgvsredqdafalrsqqrtaaaqakGffaeeivpveikqkkGeetvvdkdehl 225
                                         p +ka+yGvd+mp+ta+nva+++ vsr+dqdafalrsqqrtaaaqa+Gffaeeivpv++++kkG etvv++deh+
  lcl|FitnessBrowser__WCS417:GFF1320 152 PLMKAQYGVDAMPQTADNVADDYHVSRADQDAFALRSQQRTAAAQAAGFFAEEIVPVRVAHKKG-ETVVEQDEHP 225
                                         ****************************************************************.899******* PP

                           TIGR02430 226 raettlealaklkavvredgtvtaGnasGvndGaaalllaseeavkrhgltprarilaaasaGveprvmGlgpvp 300
                                         ra+ttleal+klk+v+ +d+tvtaGnasGvndGaaal+las+eavk+hglt+rar+l++asaGv+prvmG+gpvp
  lcl|FitnessBrowser__WCS417:GFF1320 226 RADTTLEALTKLKPVNGPDKTVTAGNASGVNDGAAALILASAEAVKKHGLTARARVLGMASAGVAPRVMGIGPVP 300
                                         *************************************************************************** PP

                           TIGR02430 301 avkkllaraglsledldvielneafaaqalavlrelgladddarvnpnGGaialGhplGasGarlvltalkqlek 375
                                         av+kl++r+gl++ d+dvielneafa+q+lavlrelg+add+ +vnpnGGaialGhplG+sGarlvltal+qlek
  lcl|FitnessBrowser__WCS417:GFF1320 301 AVRKLVERLGLAVTDFDVIELNEAFASQGLAVLRELGIADDAPQVNPNGGAIALGHPLGMSGARLVLTALHQLEK 375
                                         *************************************************************************** PP

                           TIGR02430 376 sggryalatlciGvGqGialvierv 400
                                         +gg  +lat+c+GvGqG+al+ier+
  lcl|FitnessBrowser__WCS417:GFF1320 376 TGGSKGLATMCVGVGQGLALAIERI 400
                                         ************************7 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: 11.67
//
[ok]

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

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Protein sequences (fasta format)
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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