Alignments for a candidate for ackA in Shewanella sp. ANA-3

GapMind for catabolism of small carbon sources

Alignments for a candidate for ackA in Shewanella sp. ANA-3

Align acetate kinase (EC 2.7.2.1) (characterized)
to candidate 7024360 Shewana3_1552 acetate kinase (RefSeq)

Query= BRENDA::E3W769
         (401 letters)



>FitnessBrowser__ANA3:7024360
          Length = 399

 Score =  683 bits (1762), Expect = 0.0
 Identities = 336/399 (84%), Positives = 371/399 (92%), Gaps = 1/399 (0%)

Query: 1   MSDKLVLVLNCGSSSLKFAIIDAQSGDDKISGLAECFGLEDSRIKWKFNGGKNEAKLGAF 60
           MS+KLVLVLNCGSSSLKFA+IDAQ+GDD+ISGLAECFGLEDSRIKWK NG K+E+ LGAF
Sbjct: 1   MSNKLVLVLNCGSSSLKFAVIDAQTGDDQISGLAECFGLEDSRIKWKINGEKHESSLGAF 60

Query: 61  TAHREAVEFFIVNNILAEHPELAAQIKAIGHRIVHGGEKFTRSVIITPEVIQGIEDCASL 120
           TAHREAVEF IVN ILA  PELAAQI+A+GHRIVHGGEKFTRSVII   VI+GIE+C+SL
Sbjct: 61  TAHREAVEF-IVNKILAGQPELAAQIQAVGHRIVHGGEKFTRSVIIDEHVIKGIEECSSL 119

Query: 121 APLHNPAHLIGIRAAIASFPQLPQVTVFDTAFHQTMPEKAYIYALPYKLYREHSIRRYGM 180
           APLHNPAHLIGIRAAIASFP+LPQV VFDTAFHQ+MPE+AYIYALPYKLYREH IRRYGM
Sbjct: 120 APLHNPAHLIGIRAAIASFPKLPQVAVFDTAFHQSMPERAYIYALPYKLYREHGIRRYGM 179

Query: 181 HGTSHFYVCREAAKVLGKDVKDINVICAHLGNGASVAAIKGGKSVDTSMGLTPLEGLVMG 240
           HGTSH +V REAAKVL K +++ NVICAHLGNGASV A+KGGKSVDTSMGLTPLEGLVMG
Sbjct: 180 HGTSHLFVSREAAKVLNKPLEETNVICAHLGNGASVTAVKGGKSVDTSMGLTPLEGLVMG 239

Query: 241 TRCGDLDPSIIFHLVKQLGYTLDEVNNLLNKQSGLLGISELTNDCRGIEEGYHDGHKGAT 300
           TRCGDLDPSII+HLV QLGYTL+EVNNL+NKQSGLLGISELTNDCRGIEEGY DGHKGAT
Sbjct: 240 TRCGDLDPSIIYHLVHQLGYTLEEVNNLMNKQSGLLGISELTNDCRGIEEGYADGHKGAT 299

Query: 301 LALDIFCYRLAKYIASYTVPLGRLDAIIFTGGIGENSNLIREKVLNLLEIFNFKVDDILN 360
           LAL+IFCYRLAKYIASYTVPLGRLDA++FTGGIGENS++IREKVLN+L+IFNF VD   N
Sbjct: 300 LALEIFCYRLAKYIASYTVPLGRLDAVVFTGGIGENSDIIREKVLNMLQIFNFHVDSERN 359

Query: 361 KAARFGQQGVITQAGTPIAMVIPTNEEWVIAEDAIKLIT 399
           KAARFG++G+IT   + +AMVIPTNEEWVIAED+IKLIT
Sbjct: 360 KAARFGKKGIITTDNSTVAMVIPTNEEWVIAEDSIKLIT 398


Lambda     K      H
   0.321    0.139    0.409 

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: 630
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: 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 ( 7.4 bits)
X2: 38 (14.6 bits)
X3: 64 (24.7 bits)
S1: 41 (21.8 bits)
S2: 50 (23.9 bits)

Align candidate 7024360 Shewana3_1552 (acetate kinase (RefSeq))
to HMM TIGR00016 (ackA: acetate kinase (EC 2.7.2.1))

# 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/TIGR00016.hmm
# target sequence database:        /tmp/gapView.17477.genome.faa
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Query:       TIGR00016  [M=405]
Accession:   TIGR00016
Description: ackA: acetate kinase
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
    ------- ------ -----    ------- ------ -----   ---- --  --------                         -----------
   3.4e-171  555.3   0.2   3.9e-171  555.2   0.2    1.0  1  lcl|FitnessBrowser__ANA3:7024360  Shewana3_1552 acetate kinase (Re


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__ANA3:7024360  Shewana3_1552 acetate kinase (RefSeq)
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  555.2   0.2  3.9e-171  3.9e-171       2     404 ..       2     397 ..       1     398 [. 0.98

  Alignments for each domain:
  == domain 1  score: 555.2 bits;  conditional E-value: 3.9e-171
                         TIGR00016   2 kskkilvlnaGssslkfalldaensekvllsglverikleeariktvedgekkeeeklaiedheeavkkllntlkkd 78 
                                       ++k++lvln+Gssslkfa++da++++   +sgl+e++ le++rik++ +gek+e+   a+++h+eav++++n++ + 
  lcl|FitnessBrowser__ANA3:7024360   2 SNKLVLVLNCGSSSLKFAVIDAQTGDDQ-ISGLAECFGLEDSRIKWKINGEKHESSLGAFTAHREAVEFIVNKILA- 76 
                                       6799********************9999.9*********************************************7. PP

                         TIGR00016  79 kkilkelseialiGHRvvhGgekftesvivtdevlkkikdiselAPlHnpaelegieavlklkvllkaknvavFDta 155
                                           ++ ++i+++GHR+vhGgekft svi++++v+k+i++ s+lAPlHnpa+l+gi+a+      +k ++vavFDta
  lcl|FitnessBrowser__ANA3:7024360  77 -GQPELAAQIQAVGHRIVHGGEKFTRSVIIDEHVIKGIEECSSLAPLHNPAHLIGIRAAI--ASFPKLPQVAVFDTA 150
                                       .559********************************************************..6677889******** PP

                         TIGR00016 156 fHqtipeeaylYalPyslykelgvRrYGfHGtshkyvtqraakllnkplddlnlivcHlGnGasvsavknGksidts 232
                                       fHq +pe+ay+YalPy+ly+e+g+RrYG+HGtsh +v+++aak+lnkpl++ n+i +HlGnGasv+avk Gks+dts
  lcl|FitnessBrowser__ANA3:7024360 151 FHQSMPERAYIYALPYKLYREHGIRRYGMHGTSHLFVSREAAKVLNKPLEETNVICAHLGNGASVTAVKGGKSVDTS 227
                                       ***************************************************************************** PP

                         TIGR00016 233 mGltPLeGlvmGtRsGdiDpaiisylaetlglsldeieetlnkksGllgisglssDlRdildkkeegneeaklAlkv 309
                                       mGltPLeGlvmGtR+Gd+Dp+ii +l ++lg +l+e+++++nk+sGllgis+l++D+R i++ +++g++ a+lAl++
  lcl|FitnessBrowser__ANA3:7024360 228 MGLTPLEGLVMGTRCGDLDPSIIYHLVHQLGYTLEEVNNLMNKQSGLLGISELTNDCRGIEEGYADGHKGATLALEI 304
                                       ***************************************************************************** PP

                         TIGR00016 310 yvhRiakyigkyiaslegelDaivFtgGiGenaaevrelvleklevlGlkldlelnnaarsgkesvisteeskvkvl 386
                                       +++R+akyi++y+  l g+lDa+vFtgGiGen+  +re+vl+ l+++ +++d+e+n+aar+gk+++i+t++s+v ++
  lcl|FitnessBrowser__ANA3:7024360 305 FCYRLAKYIASYTVPL-GRLDAVVFTGGIGENSDIIREKVLNMLQIFNFHVDSERNKAARFGKKGIITTDNSTV-AM 379
                                       ****************.67***************************************************9998.58 PP

                         TIGR00016 387 viptneelviaeDalrla 404
                                       viptnee+viaeD+++l+
  lcl|FitnessBrowser__ANA3:7024360 380 VIPTNEEWVIAEDSIKLI 397
                                       9**************997 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (405 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 0.00s 00:00:00.02 Elapsed: 00:00:00.02
# Mc/sec: 7.33
//
[ok]

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

Downloads

Candidates (tab-delimited)
Steps (tab-delimited)
Rules (tab-delimited)
Protein sequences (fasta format)
Organisms (tab-delimited)
SQLite3 databases

Related tools

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