Relationship between Ni(II) and Zn(II) Coordination and Nucleotide Binding by the Helicobacter pylori [NiFe]-Hydrogenase and Urease Maturation Factor HypB
Bibliographic record
Abstract
The pathogen Helicobacter pylori requires two nickel-containing enzymes, urease and [NiFe]-hydrogenase, for efficient colonization of the human gastric mucosa. These enzymes possess complex metallocenters that are assembled by teams of proteins in multistep pathways. One essential accessory protein is the GTPase HypB, which is required for Ni(II) delivery to [NiFe]-hydrogenase and participates in urease maturation. Ni(II) or Zn(II) binding to a site embedded in the GTPase domain of HypB modulates the enzymatic activity, suggesting a mechanism of regulation. In this study, biochemical and structural analyses of H. pylori HypB (HpHypB) revealed an intricate link between nucleotide and metal binding. HpHypB nickel coordination, stoichiometry, and affinity were modulated by GTP and GDP, an effect not observed for zinc, and biochemical evidence suggests that His-107 coordination to nickel toggles on and off in a nucleotide-dependent manner. These results are consistent with the crystal structure of HpHypB loaded with Ni(II), GDP, and Pi, which reveals a nickel site distinct from that of zinc-loaded Methanocaldococcus jannaschii HypB as well as subtle changes to the protein structure. Furthermore, Cys-142, a metal ligand from the Switch II GTPase motif, was identified as a key component of the signal transduction between metal binding and the enzymatic activity. Finally, potassium accelerated the enzymatic activity of HpHypB but had no effect on the other biochemical properties of the protein. Altogether, this molecular level information about HpHypB provides insight into its cellular function and illuminates a possible mechanism of metal ion discrimination.
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".