Alignments for a candidate for fadA in Caulobacter crescentus NA1000

GapMind for catabolism of small carbon sources

Alignments for a candidate for fadA in Caulobacter crescentus NA1000

Align 3-ketoacyl-CoA thiolase [isoleucine degradation] (EC 2.3.1.16) (characterized)
to candidate CCNA_00938 CCNA_00938 acetyl-CoA acetyltransferase

Query= reanno::MR1:200842
         (396 letters)



>FitnessBrowser__Caulo:CCNA_00938
          Length = 395

 Score =  494 bits (1272), Expect = e-144
 Identities = 250/388 (64%), Positives = 302/388 (77%), Gaps = 1/388 (0%)

Query: 10  IVIVAAKRTPMGSFQGSLSGITSLSLAATAIKALLADTQVAPDKVDEVLMGCVLPAGLGQ 69
           +VIVA  RTPMG FQG+L G+ +  L ATA+KA +    VA DKV++++MGCVLPAGLGQ
Sbjct: 7   VVIVAYARTPMGGFQGALGGVKATDLGATAVKAAIERAGVAGDKVEQIIMGCVLPAGLGQ 66

Query: 70  APARQATLGAGLPLSVGATTVNKVCGSGMKTVMLAHDLIKAGSAKVVIAGGMESMSQAPY 129
           APARQA LGAGLPLSV ATTVNK+CGSGM+  ++AHD + AGS  VV+AGGMESM+ APY
Sbjct: 67  APARQAALGAGLPLSVEATTVNKMCGSGMQAAIMAHDALAAGSVDVVVAGGMESMTGAPY 126

Query: 130 LLDKARAGIRMGHGKVLDHMFLDGLEDAYTGGA-MGTFAQKTADEFGITREQMDAFALSS 188
           L+ K RAG R+GH ++ D M+LDGLEDAYT G  MG FA+ +A  +  TRE MD +A   
Sbjct: 127 LMSKHRAGARIGHDQMWDSMYLDGLEDAYTPGKLMGAFAEDSAQTYQFTREAMDDYATRG 186

Query: 189 LEKANAAINSGAFKTEIVPVTVSDRRGDVTIDTDEQPGNARPEKIPTLRPAFAKDGTITA 248
           L KA AA+ SGAFK EI PV+V  R+G   +DTDEQPG A P KIPTLRPAF++DG +TA
Sbjct: 187 LMKAKAAVESGAFKAEITPVSVVTRKGTEVVDTDEQPGKADPAKIPTLRPAFSRDGGVTA 246

Query: 249 ANSSSISDGAAALMLTTRANAEQLGLTVLATIKGHTTHAQEPALFTTAPVGAMAKLLSNV 308
           ANSSSISDGAAAL++T  + A+ LGL +LA +  H  HA EP LFTTAPV AM K L   
Sbjct: 247 ANSSSISDGAAALVMTRESVAKALGLPILAKVVSHAAHAHEPGLFTTAPVPAMQKALKKA 306

Query: 309 GWSKDEVDLFEINEAFAMVTMLAVSELGLDMTKVNVNGGACALGHPIGCSGARLLVTLIH 368
           GWS  +VDLFE+NEAFA+V M+A  ELG+   K+NVNGGACALGHPIG SGAR+L TLI+
Sbjct: 307 GWSVADVDLFEVNEAFAVVAMIAQKELGIPDDKLNVNGGACALGHPIGASGARILCTLIN 366

Query: 369 ALKARGLKRGVASLCIGGGEATAMAIEV 396
           AL++RG K+G+ASLCIGGGEATAMAIE+
Sbjct: 367 ALQSRGGKKGLASLCIGGGEATAMAIEL 394


Lambda     K      H
   0.316    0.131    0.364 

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: 525
Number of extensions: 16
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: 396
Length of database: 395
Length adjustment: 31
Effective length of query: 365
Effective length of database: 364
Effective search space:   132860
Effective search space used:   132860
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_00938 CCNA_00938 (acetyl-CoA acetyltransferase)
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.19512.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
    ------- ------ -----    ------- ------ -----   ---- --  --------                             -----------
   7.4e-131  422.5  11.4   8.3e-131  422.4  11.4    1.0  1  lcl|FitnessBrowser__Caulo:CCNA_00938  CCNA_00938 acetyl-CoA acetyltran


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__Caulo:CCNA_00938  CCNA_00938 acetyl-CoA acetyltransferase
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  422.4  11.4  8.3e-131  8.3e-131       1     385 []       9     393 ..       9     393 .. 0.98

  Alignments for each domain:
  == domain 1  score: 422.4 bits;  conditional E-value: 8.3e-131
                             TIGR01930   1 ivdavRtpigklggslkelsaedLlaavikelleragldpekidevilGnvlqageqaniaReaalaaglpes 73 
                                           iv+++Rtp+g ++g+l  ++a dL+a+++k+++erag+  +k++++i+G+vl+ag ++++aR+aal aglp s
  lcl|FitnessBrowser__Caulo:CCNA_00938   9 IVAYARTPMGGFQGALGGVKATDLGATAVKAAIERAGVAGDKVEQIIMGCVLPAGLGQAPARQAALGAGLPLS 81 
                                           89********99************************************************************* PP

                             TIGR01930  74 vpaltvnrvCaSglqAvalaaqkikaGeadvvvaGGvEsmSrvpillkaslrreslklgkakledqllkdl.. 144
                                           v+a+tvn++C+Sg+qA ++a ++++aG  dvvvaGG+Esm+ +p+l++++  r+++++g+ ++ d++  d+  
  lcl|FitnessBrowser__Caulo:CCNA_00938  82 VEATTVNKMCGSGMQAAIMAHDALAAGSVDVVVAGGMESMTGAPYLMSKH--RAGARIGHDQMWDSMYLDGle 152
                                           **********************************************9997..899999999999999888889 PP

                             TIGR01930 145 ..vktklsmgetAenlakkygisReeqDeyalrShqkaakAieegkfkdeivpvevkgk..kkvvskDegirp 213
                                              + +  mg  Ae  a++y+++Re +D+ya r  +ka++A+e+g fk+ei+pv+v ++  ++vv++De++  
  lcl|FitnessBrowser__Caulo:CCNA_00938 153 daYTPGKLMGAFAEDSAQTYQFTREAMDDYATRGLMKAKAAVESGAFKAEITPVSVVTRkgTEVVDTDEQPG- 224
                                           9888899*************************************************9999999999999987. PP

                             TIGR01930 214 nttlekLakLkpafkekkgstvtAgNssqlnDGAaalllmseevakelgltplarivsaavagvdpeemglgp 286
                                           +   +k+ +L+paf + +g  vtA+Nss+++DGAaal+++ e+vak+lgl +la++vs+a  +++p  ++++p
  lcl|FitnessBrowser__Caulo:CCNA_00938 225 KADPAKIPTLRPAFSR-DGG-VTAANSSSISDGAAALVMTRESVAKALGLPILAKVVSHAAHAHEPGLFTTAP 295
                                           8899**********97.885.**************************************************** PP

                             TIGR01930 287 vpAiekaLkkaglsisdidlvEinEAFAaqvlavekelgsldlekvNvnGGAiAlGHPlGasGarivltllke 359
                                           vpA++kaLkkag+s+ d+dl+E+nEAFA+++++++kelg + ++k+NvnGGA AlGHP+GasGari+ tl+++
  lcl|FitnessBrowser__Caulo:CCNA_00938 296 VPAMQKALKKAGWSVADVDLFEVNEAFAVVAMIAQKELG-IPDDKLNVNGGACALGHPIGASGARILCTLINA 367
                                           ***************************************.88******************************* PP

                             TIGR01930 360 LkergkkyGlatlCvggGqGaAvile 385
                                           L+ rg+k Gla+lC+ggG ++A+ +e
  lcl|FitnessBrowser__Caulo:CCNA_00938 368 LQSRGGKKGLASLCIGGGEATAMAIE 393
                                           ***********************987 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (385 nodes)
Target sequences:                          1  (395 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: 9.78
//
[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