Alignments for a candidate for ackA in Dechlorosoma suillum PS

GapMind for catabolism of small carbon sources

Alignments for a candidate for ackA in Dechlorosoma suillum PS

Align acetate kinase (EC 2.7.2.1) (characterized)
to candidate Dsui_3033 Dsui_3033 acetate kinase

Query= BRENDA::G4T0C7
         (401 letters)



>FitnessBrowser__PS:Dsui_3033
          Length = 397

 Score =  279 bits (713), Expect = 1e-79
 Identities = 167/405 (41%), Positives = 249/405 (61%), Gaps = 28/405 (6%)

Query: 8   ILVINSGSSSIKYRLIALPQ------EQVLADGLLERIGEQESRIIHRADDSGR-LNEIK 60
           +L +N+GSSSIK+ L  L        E VL+ G ++ IG +    I   DD G+ L+++ 
Sbjct: 5   VLTLNAGSSSIKFSLYELENGGGIAAEPVLS-GQIDGIGVKAH--IKAKDDKGKKLDDVD 61

Query: 61  ---QSVIAADHHQAFKAVFEILGEN---CSVDAIGHRVVHGGDRFSGPALVDDDTIASMR 114
              Q    + HH+A + +   L ++     + A+GHRVVHGG+R+S P  +DD+ I  + 
Sbjct: 62  IPLQGDAESQHHEALEFLISWLHDHEKGWKIVAVGHRVVHGGERYSRPMKLDDNIIEHLT 121

Query: 115 ALCRIAPLHNPVNLLGIESCLAHFPGVPQVAVFDTAFHQTMPPHAYRYAIPETWYSDYGI 174
            L  +APLH P NL G+++     P VPQVA FDTAFH+T  P A  +A+P  W +  G+
Sbjct: 122 RLIPLAPLHQPHNLDGVDALRTMMPEVPQVACFDTAFHRTQAPVAQAFALPR-WITGEGV 180

Query: 175 RRFGFHGTSHHYVARRAAEFIGKPFDRSHLITLHLGNGASATAIANGRSVDTSMGFTPLE 234
           +R+GFHG S+ Y+AR   ++   P     ++  HLGNGAS   + + +S  ++MGFT +E
Sbjct: 181 KRYGFHGLSYEYIARVLPDY--SPRANGRVVVAHLGNGASMCGMVDRKSQVSTMGFTAVE 238

Query: 235 GLVMGTRSGDLDPAIPLFVEQTENTDTDAIDRALNRESGLKGLCGTN-DLRTVLEQTNAG 293
           GL+MGTR+G LDP + L++ + +  D  A+ + L +ESGL G+ G + D+RT+L    A 
Sbjct: 239 GLMMGTRTGALDPGVMLYLMENKGMDVKALTKLLYKESGLLGVSGISPDMRTLL----AS 294

Query: 294 DE-RARLALDLYCYRIKKYIGAYYAVLGEVDALVFTGGVGENAAEVRRLACEGLSRLGIA 352
           D+  A+ A+DL+CYR+ + +G+  A +G +DALVFTGG+GE+AAEVRR  C   S LGI+
Sbjct: 295 DKPEAKEAVDLFCYRVVRELGSLAAAIGGIDALVFTGGIGEHAAEVRRRVCLQASWLGIS 354

Query: 353 IDEAANSDVTGAIAEIGLAESRTRILVIKTDEELQIAREAMAVLD 397
           IDE+AN+     I+E     S   +LVI T+EE  IAR    +L+
Sbjct: 355 IDESANALHANRISE---PRSTVDVLVIPTNEEWMIARHTATLLN 396


Lambda     K      H
   0.320    0.137    0.400 

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: 441
Number of extensions: 22
Number of successful extensions: 6
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: 397
Length adjustment: 31
Effective length of query: 370
Effective length of database: 366
Effective search space:   135420
Effective search space used:   135420
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 Dsui_3033 Dsui_3033 (acetate kinase)
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.32752.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
    ------- ------ -----    ------- ------ -----   ---- --  --------                         -----------
   8.8e-114  366.3   0.0   9.9e-114  366.1   0.0    1.0  1  lcl|FitnessBrowser__PS:Dsui_3033  Dsui_3033 acetate kinase


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__PS:Dsui_3033  Dsui_3033 acetate kinase
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  366.1   0.0  9.9e-114  9.9e-114       4     403 ..       3     394 ..       1     396 [. 0.92

  Alignments for each domain:
  == domain 1  score: 366.1 bits;  conditional E-value: 9.9e-114
                         TIGR00016   4 kkilvlnaGssslkfalldaen....sekvllsglverikleeariktvedgekkeeek......laiedheeavkk 70 
                                       + +l lnaGsss+kf+l++ en    + + +lsg ++ i ++   ik + d +kk  ++       a ++h+ea+++
  lcl|FitnessBrowser__PS:Dsui_3033   3 RAVLTLNAGSSSIKFSLYELENgggiAAEPVLSGQIDGIGVKAH-IKAKDDKGKKLDDVdiplqgDAESQHHEALEF 78 
                                       579*******************555534448**********998.66665555554432112211356789****** PP

                         TIGR00016  71 llntlkkdkkilkelseialiGHRvvhGgekftesvivtdevlkkikdiselAPlHnpaelegieavlklkvllkak 147
                                       l++ l++    +++  +i ++GHRvvhGge+++  + ++d++++++ ++++lAPlH p +l+g++a+   +++++++
  lcl|FitnessBrowser__PS:Dsui_3033  79 LISWLHD----HEKGWKIVAVGHRVVHGGERYSRPMKLDDNIIEHLTRLIPLAPLHQPHNLDGVDALR--TMMPEVP 149
                                       ******6....588999***************************************************..999**** PP

                         TIGR00016 148 nvavFDtafHqtipeeaylYalPyslykelgvRrYGfHGtshkyvtqraakllnkplddlnlivcHlGnGasvsavk 224
                                       +va+FDtafH+t +  a  +alP ++ +  gv+rYGfHG+s++y+++ + ++  +  ++ +++v+HlGnGas++ + 
  lcl|FitnessBrowser__PS:Dsui_3033 150 QVACFDTAFHRTQAPVAQAFALP-RWITGEGVKRYGFHGLSYEYIARVLPDYSPR--ANGRVVVAHLGNGASMCGMV 223
                                       ***********************.9**************************9877..999***************** PP

                         TIGR00016 225 nGksidtsmGltPLeGlvmGtRsGdiDpaiisylaetlglsldeieetlnkksGllgisglssDlRdildkkeegne 301
                                       + ks   +mG+t +eGl+mGtR+G +Dp+++ yl+e++g+ +++++++l k+sGllg+sg+s D+R++l+     + 
  lcl|FitnessBrowser__PS:Dsui_3033 224 DRKSQVSTMGFTAVEGLMMGTRTGALDPGVMLYLMENKGMDVKALTKLLYKESGLLGVSGISPDMRTLLASD---KP 297
                                       ********************************************************************9887...78 PP

                         TIGR00016 302 eaklAlkvyvhRiakyigkyiaslegelDaivFtgGiGenaaevrelvleklevlGlkldlelnnaarsgkesvist 378
                                       eak A++++++R+ + +g+ +a++ g +Da+vFtgGiGe aaevr++v+ +   lG+ +d+  n      + + is+
  lcl|FitnessBrowser__PS:Dsui_3033 298 EAKEAVDLFCYRVVRELGSLAAAIGG-IDALVFTGGIGEHAAEVRRRVCLQASWLGISIDESANA----LHANRISE 369
                                       89**********************76.*********************************99998....77889*** PP

                         TIGR00016 379 eeskvkvlviptneelviaeDalrl 403
                                       + s+v vlviptnee++ia+ ++ l
  lcl|FitnessBrowser__PS:Dsui_3033 370 PRSTVDVLVIPTNEEWMIARHTATL 394
                                       *******************998876 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (405 nodes)
Target sequences:                          1  (397 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: 00:00:00.01
# Mc/sec: 11.15
//
[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