Ligand binding specificity of the <i>Escherichia coli</i> periplasmic histidine binding protein, HisJ
Bibliographic record
Abstract
The HisJ protein from Escherichia coli and related Gram negative bacteria is the periplasmic component of a bacterial ATP-cassette (ABC) transporter system. Together these proteins form a transmembrane complex that can take up L-histidine from the environment and translocate it into the cytosol. We have studied the specificity of HisJ for binding L-His and many related naturally occurring compounds. Our data confirm that L-His is the preferred ligand, but that 1-methyl-L-His and 3-methyl-L-His can also bind, while the dipeptide carnosine binds weakly and D-histidine and the histidine degradation products, histamine, urocanic acid and imidazole do not bind. L-Arg, homo-L-Arg, and post-translationally modified methylated Arg-analogs also bind with reasonable avidity, with the exception of symmetric dimethylated-L-Arg. In contrast, L-Lys and L-Orn have considerably weaker interactions with HisJ and methylated and acetylated Lys variants show relatively poor binding. It was also observed that the carboxylate group of these amino acids and their variants was very important for proper recognition of the ligand. Taken together our results are a key step towards designing HisJ as a specific protein-based reagentless biosensor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".