Plastic recognition and electrogenic uniport translocation of 1st-, 2nd-, and 3rd-row transition and post-transition metals by primary-active transmembrane P1B-2-type ATPase pumps
Bibliographic record
Abstract
Transmembrane P1B-type ATPase pumps catalyze the extrusion of transition metal ions across cellular lipid membranes to maintain essential cellular metal homeostasis and detoxify toxic metals. Zn(II)-pumps of the P1B-2-type subclass, in addition to Zn2+, select diverse metals (Pb2+, Cd2+ and Hg2+) at their transmembrane binding site and feature promiscuous metal-dependent ATP hydrolysis in the presence of these metals. Yet, a comprehensive understanding of the transport of these metals, their relative translocation rates, and transport mechanism remains elusive. We developed a platform for the characterization of primary-active Zn(II)-pumps in proteoliposomes to study metal selectivity, translocation events and transport mechanism in real-time, employing a “multi-probe” approach with fluorescent sensors responsive to diverse stimuli (metals, pH and membrane potential). Together with atomic-resolution investigation of cargo selection by X-ray Absorption Spectroscopy (XAS), we demonstrate that Zn(II)-pumps are electrogenic uniporters that preserve the transport mechanism with 1st-, 2nd- and 3rd-row transition metal substrates. Promiscuous coordination plasticity, guarantees diverse, yet defined, cargo selectivity coupled to their translocation.
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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".