GapMind for catabolism of small carbon sources


Finding step aatQ for L-asparagine catabolism in Sinorhizobium meliloti 1021

5 candidates for aatQ: aspartate/asparagine ABC transporter, permease component 1 (AatQ)

Score Gene Description Similar to Id. Cov. Bits Other hit Other id. Other bits
lo SMc03865 amino-acid transport system permease ABC transporter protein PP1070, component of Acidic amino acid uptake porter, AatJMQP (characterized) 30% 85% 117.5 Basic amino acid uptake transporter, BgtAB 39% 158.3
lo SMa0079 ABC transporter permease PP1070, component of Acidic amino acid uptake porter, AatJMQP (characterized) 32% 86% 114 Glutamine transport system permease protein GlnP aka B0810, component of Three component ABC L-glutamine porter. The basal ATPase activity (ATP hydrolysis in the absence of substrate) is mainly caused by the docking of the closed-unliganded state of GlnH onto the transporter domain of GlnPQ. Unlike glutamine, arginine binds both GlnH domains, but does not trigger their closing. Comparison of the ATPase activity in nanodiscs with glutamine transport in proteoliposomes suggested that the stoichiometry of ATP per substrate is close to two 36% 141.7
lo SMa2197 ABC transporter permease ABC transporter for L-asparagine and L-glutamate, permease component 1 (characterized) 31% 96% 113.6 BgtB aka GLNH aka SLL1270, component of Arginine/lysine/histidine/glutamine porter 34% 136.7
lo SMc03133 amino-acid transport system permease ABC transporter protein ABC transporter for L-aspartate, L-asparagine, L-glutamate, and L-glutamine, permease component 2 (characterized) 32% 87% 110.9 L-cystine transport system permease protein YecS 38% 148.7
lo SM_b20429 amino acid ABC transporter permease Glutamate/aspartate import permease protein GltJ (characterized) 32% 88% 97.8 ABC transporter for L-Histidine, permease component 1 38% 142.5

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 aatQ

Or cluster all characterized aatQ proteins

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



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:

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