Alignments for a candidate for fru2-IIA in Shewanella oneidensis MR-1

GapMind for catabolism of small carbon sources

Alignments for a candidate for fru2-IIA in Shewanella oneidensis MR-1

Align PTS system, fructose-specific, IIA component, component of D-allose/D-ribose transporting Enzyme II complex (Fru2; IIA/IIB/IIC) (Patron et al. 2017). This system is similar to Frz of E. coli (TC#4.A.2.1.9) which is involved in environmental sensing, host adaptation and virulence (characterized)
to candidate 203047 SO3963 nitrogen regulatory IIA protein (NCBI ptt file)

Query= TCDB::Q3JZE3
         (149 letters)



>FitnessBrowser__MR1:203047
          Length = 147

 Score = 61.6 bits (148), Expect = 5e-15
 Identities = 33/101 (32%), Positives = 57/101 (56%), Gaps = 2/101 (1%)

Query: 46  KDIYLRESEGQTGIGNYIAIPHGKSSHVTEPAVVIGINSSEIPWETLDDNGVKVIVLFAV 105
           + +  RE  G TGIGN IAIPHG+ + +T+P  ++      I ++ +D   V   +LFA+
Sbjct: 45  ESLVAREKMGSTGIGNGIAIPHGRLTDITQPIAILIKCEEPIAFDAIDKQPVD--ILFAL 102

Query: 106 GNDSIAAQEHLKILSLFARKLGNDQIVEKLINAKNEDDVIE 146
              +   Q+HL  LS  A KL + Q++++L    +E ++ +
Sbjct: 103 LVPANQCQQHLSTLSCMAEKLSDKQVLKQLRKTHDETELYQ 143


Lambda     K      H
   0.314    0.134    0.365 

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: 54
Number of extensions: 2
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: 149
Length of database: 147
Length adjustment: 16
Effective length of query: 133
Effective length of database: 131
Effective search space:    17423
Effective search space used:    17423
Neighboring words threshold: 11
Window for multiple hits: 40
X1: 16 ( 7.2 bits)
X2: 38 (14.6 bits)
X3: 64 (24.7 bits)
S1: 42 (21.9 bits)
S2: 42 (20.8 bits)

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