GapMind for catabolism of small carbon sources

 

Finding step serP for L-serine catabolism in Escherichia coli BW25113

4 candidates for serP: L-serine permease SerP

Score Gene Description Similar to Id. Cov. Bits Other hit Other id. Other bits
med b4208 D-alanine/D-serine/glycine transporter (NCBI) Serine transporter, SerP2 or YdgB, of 459 aas and 12 TMSs (Trip et al. 2013). Transports L-alanine (Km = 20 μM), D-alanine (Km = 38 μM), L-serine, D-serine (Km = 356 μM) and glycine (Noens and Lolkema 2015). The encoding gene is adjacent to the one encoding SerP1 (TC# 2.A.3.1.21) (characterized) 42% 99% 359.4 D-serine/L-alanine/D-alanine/glycine/D-cycloserine uptake porter of 556 aas, CycA 60% 572.8
lo b0112 aromatic amino acid transporter (NCBI) Serine uptake transporter, SerP1, of 259 aas and 12 TMSs (Trip et al. 2013). L-serine is the highest affinity substrate (Km = 18 μM), but SerP1 also transports L-threonine and L-cysteine (Km values = 20 - 40 μM) (characterized) 35% 100% 302.8 Aromatic amino acid:H+ symporter, AroP of 457 aas and 12 TMSs (Cosgriff and Pittard 1997). Transports phenylalanine, tyrosine and tryptophan 100% 905.2
lo b3795 predicted transporter (NCBI) Serine uptake transporter, SerP1, of 259 aas and 12 TMSs (Trip et al. 2013). L-serine is the highest affinity substrate (Km = 18 μM), but SerP1 also transports L-threonine and L-cysteine (Km values = 20 - 40 μM) (characterized) 38% 99% 300.1 L-alanine and D-alanine permease 51% 476.9
lo b0576 phenylalanine transporter (NCBI) Serine transporter, SerP2 or YdgB, of 459 aas and 12 TMSs (Trip et al. 2013). Transports L-alanine (Km = 20 μM), D-alanine (Km = 38 μM), L-serine, D-serine (Km = 356 μM) and glycine (Noens and Lolkema 2015). The encoding gene is adjacent to the one encoding SerP1 (TC# 2.A.3.1.21) (characterized) 35% 99% 299.7 Phenylalanine:H+ symporter, PheP of 458 aas and 12 established TMSs 100% 912.1

Confidence: high confidence medium confidence low confidence
transporter – transporters and PTS systems are shaded because predicting their specificity is particularly challenging.

GapMind searches the predicted proteins for candidates by using ublast (a fast alternative to protein BLAST) to find similarities to characterized proteins or by using HMMer to find similarities to enzyme models (usually from TIGRFams). For alignments to characterized proteins (from ublast), scores of 44 bits correspond to an expectation value (E) of about 0.001.

Also see fitness data for the candidates

Definition of step serP

Or cluster all characterized serP proteins

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:

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:

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:

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, or view the source code.

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