Aligments for a candidate for fadA in Caulobacter crescentus NA1000

GapMind for catabolism of small carbon sources

Aligments for a candidate for fadA in Caulobacter crescentus NA1000

Align subunit of β-ketoadipyl CoA thiolase (EC 2.3.1.174; EC 2.3.1.16) (characterized)
to candidate CCNA_02490 CCNA_02490 3-ketoacyl-CoA thiolase

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



>FitnessBrowser__Caulo:CCNA_02490
          Length = 399

 Score =  564 bits (1454), Expect = e-165
 Identities = 286/400 (71%), Positives = 331/400 (82%), Gaps = 1/400 (0%)

Query: 1   MRDVFICDAIRTPIGRFGGALAGVRADDLAAVPLKALIEPNPAVQWDQVDEVFFGCANQA 60
           M   +ICD IRTPIGR+GG+L+ VRADDLAA+PLKAL+  NP++    +DE+  G ANQA
Sbjct: 1   MTAAYICDGIRTPIGRYGGSLSKVRADDLAAIPLKALVARNPSLDLAAIDEIVLGSANQA 60

Query: 61  GEDNRNVARMALLLAGLPESIPGVTLNRLCASGMDAIGTAFRAIASGEMELAIAGGVESM 120
           GEDNRNVARMALLLAG P S+PGVT+NRLCASG++A+G A RAIASG  +L IAGGVESM
Sbjct: 61  GEDNRNVARMALLLAGYPVSVPGVTVNRLCASGLEAVGYAARAIASGHNDLVIAGGVESM 120

Query: 121 SRAPFVMGKAESGYSRNMKLEDTTIGWRFINPLMKSQYGVDSMPETADNVADDYQVSRAD 180
           SRAPFVMGKA+S +SR+ ++ DTTIGWRF+NP M+  YGVDSMPETA+NVA DY V+R D
Sbjct: 121 SRAPFVMGKADSAFSRSAEIFDTTIGWRFVNPAMRKLYGVDSMPETAENVATDYGVNRED 180

Query: 181 QDAFALRSQQKAAAAQAAGFFAEEIVPVRIAHKKGETIVERDEHLRPETTLEALTKLKPV 240
           QDAFALRSQ + AAAQA GF A EI PV I  K G TIV+RDEH R ETT+EAL KLKP+
Sbjct: 181 QDAFALRSQARTAAAQANGFLAGEITPVEIPGKAGPTIVDRDEHPR-ETTMEALAKLKPI 239

Query: 241 NGPDKTVTAGNASGVNDGAAALILASAEAVKKHGLTPRARVLGMASGGVAPRVMGIGPVP 300
                TVTAGNASGVNDGA AL++AS +AVK+HGLTPRAR+ G AS GV PRVMGIGPVP
Sbjct: 240 VREGGTVTAGNASGVNDGAVALVIASEDAVKRHGLTPRARITGYASAGVEPRVMGIGPVP 299

Query: 301 AVRKLTERLGVAVSDFDVIELNEAFASQGLAVLRELGVADDAPQVNPNGGAIALGHPLGM 360
           AVRKL  + G+A+ DFDV+ELNEAFA+QGLAVLR+LG+ DD   VN NGGAIALGHPLG 
Sbjct: 300 AVRKLMAKTGLAIGDFDVVELNEAFAAQGLAVLRQLGLPDDGAHVNANGGAIALGHPLGA 359

Query: 361 SGARLVLTALHQLEKSGGRKGLATMCVGVGQGLALAIERV 400
           SGARLVLTAL QLE SGG++GLAT+C+GVGQG ALA ERV
Sbjct: 360 SGARLVLTALRQLEASGGQRGLATLCIGVGQGAALAFERV 399


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: 551
Number of extensions: 17
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: 399
Length adjustment: 31
Effective length of query: 369
Effective length of database: 368
Effective search space:   135792
Effective search space used:   135792
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 CCNA_02490 CCNA_02490 (3-ketoacyl-CoA thiolase)
to HMM TIGR01930 (acetyl-CoA C-acyltransferase (EC 2.3.1.16))

# 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/TIGR01930.hmm
# target sequence database:        /tmp/gapView.30766.genome.faa
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Query:       TIGR01930  [M=385]
Accession:   TIGR01930
Description: AcCoA-C-Actrans: acetyl-CoA C-acyltransferase
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
    ------- ------ -----    ------- ------ -----   ---- --  --------                             -----------
     8e-139  448.8   2.8     9e-139  448.6   2.8    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 !  448.6   2.8    9e-139    9e-139       1     385 []       6     397 ..       6     397 .. 0.95

  Alignments for each domain:
  == domain 1  score: 448.6 bits;  conditional E-value: 9e-139
                             TIGR01930   1 ivdavRtpigklggslkelsaedLlaavikellera.gldpekidevilGnvlqageq.aniaReaalaaglp 71 
                                           i d++Rtpig++ggsl++++a+dL+a  +k+l++r+  ld ++ide++lG + qage+  n+aR+a+l ag p
  lcl|FitnessBrowser__Caulo:CCNA_02490   6 ICDGIRTPIGRYGGSLSKVRADDLAAIPLKALVARNpSLDLAAIDEIVLGSANQAGEDnRNVARMALLLAGYP 78 
                                           789*******99**********************************************9************** PP

                             TIGR01930  72 esvpaltvnrvCaSglqAvalaaqkikaGeadvvvaGGvEsmSrvpillkaslrreslklgkakledqllkdl 144
                                            svp++tvnr+CaSgl+Av  aa++i++G++d+v+aGGvEsmSr+p+++ ++   +s+  ++a+  d+++  +
  lcl|FitnessBrowser__Caulo:CCNA_02490  79 VSVPGVTVNRLCASGLEAVGYAARAIASGHNDLVIAGGVESMSRAPFVMGKA---DSAFSRSAEIFDTTI--G 146
                                           ***********************************************99997...444445555555333..1 PP

                             TIGR01930 145 ..........vktklsmgetAenlakkygisReeqDeyalrShqkaakAieegkfkdeivpvevkgk..kkvv 205
                                                      +   sm etAen+a++yg++Re+qD++alrS+ ++a+A+++g+++ ei+pve++gk  +++v
  lcl|FitnessBrowser__Caulo:CCNA_02490 147 wrfvnpamrkLYGVDSMPETAENVATDYGVNREDQDAFALRSQARTAAAQANGFLAGEITPVEIPGKagPTIV 219
                                           3467888888667789********************************************************* PP

                             TIGR01930 206 skDegirpnttlekLakLkpafkekkgstvtAgNssqlnDGAaalllmseevakelgltplarivsaavagvd 278
                                           ++De++r +tt+e+LakLkp+ +e  g tvtAgN+s++nDGA+al+++se+++k++gltp ari ++a agv+
  lcl|FitnessBrowser__Caulo:CCNA_02490 220 DRDEHPR-ETTMEALAKLKPIVRE-GG-TVTAGNASGVNDGAVALVIASEDAVKRHGLTPRARITGYASAGVE 289
                                           *******.89************96.68.7******************************************** PP

                             TIGR01930 279 peemglgpvpAiekaLkkaglsisdidlvEinEAFAaqvlavekelgsld.lekvNvnGGAiAlGHPlGasGa 350
                                           p++mg+gpvpA++k+++k+gl i d+d+vE+nEAFAaq lav ++lg+ d  + vN nGGAiAlGHPlGasGa
  lcl|FitnessBrowser__Caulo:CCNA_02490 290 PRVMGIGPVPAVRKLMAKTGLAIGDFDVVELNEAFAAQGLAVLRQLGLPDdGAHVNANGGAIALGHPLGASGA 362
                                           ***********************************************854699******************** PP

                             TIGR01930 351 rivltllkeLkergkkyGlatlCvggGqGaAvile 385
                                           r+vlt l++L+  g+++GlatlC+g GqGaA+ +e
  lcl|FitnessBrowser__Caulo:CCNA_02490 363 RLVLTALRQLEASGGQRGLATLCIGVGQGAALAFE 397
                                           *******************************9886 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (385 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: 0.02u 0.01s 00:00:00.03 Elapsed: 00:00:00.02
# Mc/sec: 6.99
//
[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