Alignments for a candidate for tpi in Dechlorosoma suillum PS

GapMind for catabolism of small carbon sources

Alignments for a candidate for tpi in Dechlorosoma suillum PS

Align triose-phosphate isomerase (EC 5.3.1.1) (characterized)
to candidate Dsui_3052 Dsui_3052 triosephosphate isomerase

Query= BRENDA::P0A858
         (255 letters)



>FitnessBrowser__PS:Dsui_3052
          Length = 241

 Score =  240 bits (613), Expect = 2e-68
 Identities = 128/238 (53%), Positives = 163/238 (68%), Gaps = 3/238 (1%)

Query: 12  LNGSRHMVHELVSNLRKELAGVAGCAVAIAPPEMYIDMAKREAEGSHIMLGAQNVDLNLS 71
           L  +R ++  +V+ + K LA    CAV +  P  Y+  A++E   + +  GAQN+   +S
Sbjct: 5   LAANRSLLQGVVAGV-KGLAAAVECAVCVPYP--YLAQAQQELAKTEVSWGAQNLSEQVS 61

Query: 72  GAFTGETSAAMLKDIGAQYIIIGHSERRTYHKESDELIAKKFAVLKEQGLTPVLCIGETE 131
           GAFTGE SAAMLKD G +Y+I+GHSERR  + E D L+AKKF      GL P+LC+GET 
Sbjct: 62  GAFTGEVSAAMLKDFGCRYVIVGHSERRALYGEDDGLVAKKFKQALAGGLVPILCVGETL 121

Query: 132 AENEAGKTEEVCARQIDAVLKTQGAAAFEGAVIAYEPVWAIGTGKSATPAQAQAVHKFIR 191
           A+ E+G+  EV A Q+DAVL   G   F GAV+AYEPVWAIGTG +A+PAQAQAVH+ IR
Sbjct: 122 AQRESGQAAEVVAAQLDAVLDAAGVEVFAGAVVAYEPVWAIGTGLTASPAQAQAVHEGIR 181

Query: 192 DHIAKVDANIAEQVIIQYGGSVNASNAAELFAQPDIDGALVGGASLKADAFAVIVKAA 249
             +A  +A +A  V + YGGSV A NAAELFA PDIDGALVGGASL A+ F  I +AA
Sbjct: 182 ARLAARNAEVAAGVRVLYGGSVKAGNAAELFAMPDIDGALVGGASLVAEEFVAICRAA 239


Lambda     K      H
   0.316    0.130    0.366 

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: 195
Number of extensions: 9
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: 255
Length of database: 241
Length adjustment: 24
Effective length of query: 231
Effective length of database: 217
Effective search space:    50127
Effective search space used:    50127
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: 46 (22.3 bits)

Align candidate Dsui_3052 Dsui_3052 (triosephosphate isomerase)
to HMM TIGR00419 (tpiA: triose-phosphate isomerase (EC 5.3.1.1))

# 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/TIGR00419.hmm
# target sequence database:        /tmp/gapView.3211511.genome.faa
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Query:       TIGR00419  [M=228]
Accession:   TIGR00419
Description: tim: triose-phosphate isomerase
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
    ------- ------ -----    ------- ------ -----   ---- --  --------                     -----------
    4.6e-68  215.5   5.9    5.3e-68  215.2   5.9    1.0  1  FitnessBrowser__PS:Dsui_3052  


Domain annotation for each sequence (and alignments):
>> FitnessBrowser__PS:Dsui_3052  
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  215.2   5.9   5.3e-68   5.3e-68      30     228 .]      24     230 ..       3     230 .. 0.94

  Alignments for each domain:
  == domain 1  score: 215.2 bits;  conditional E-value: 5.3e-68
                     TIGR00419  30 agvevavappfvdldvvkdeve.seiqvaAqnvdavksGaftGeisAemlkdlGakgvligHsErRsllkeadeliekkva 109
                                   a ve av +p+ +l  +++e+  +e++ +Aqn+  + sGaftGe+sA+mlkd+G+++v++gHsErR+l  e d l++kk+ 
  FitnessBrowser__PS:Dsui_3052  24 AAVECAVCVPYPYLAQAQQELAkTEVSWGAQNLSEQVSGAFTGEVSAAMLKDFGCRYVIVGHSERRALYGEDDGLVAKKFK 104
                                   5688999*************9889********************************************************* PP

                     TIGR00419 110 rlkelglksvvCvgetleere.......aartinnvattaaaaAlepdvvAvEPveliGtGkpvskAeaevveksvrdhlk 183
                                   ++ + gl +++Cvgetl++re       +a ++++v++ a++  + ++vvA+EPv++iGtG ++s+A+a++v++ +r  l+
  FitnessBrowser__PS:Dsui_3052 105 QALAGGLVPILCVGETLAQREsgqaaevVAAQLDAVLDAAGVEVFAGAVVAYEPVWAIGTGLTASPAQAQAVHEGIRARLA 185
                                   *********************88888887888888888888888************************************* PP

                     TIGR00419 184 kvskevaesvrvlyGasvtaaedaelaaqldvdGvLlasavlkae 228
                                     ++eva  vrvlyG+sv+a+++ael+a +d+dG+L+++a+l ae
  FitnessBrowser__PS:Dsui_3052 186 ARNAEVAAGVRVLYGGSVKAGNAAELFAMPDIDGALVGGASLVAE 230
                                   ******************************************986 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (228 nodes)
Target sequences:                          1  (241 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: 0.00u 0.00s 00:00:00.00 Elapsed: 00:00:00.00
# Mc/sec: 21.71
//
[ok]

This GapMind analysis is from Apr 09 2024. 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 2024 update 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