V-ATPase Subunit Interactions: The Long Road to Therapeutic Targeting
Bibliographic record
Abstract
Over the last three decades, V-ATPases have emerged from the obscurity of poorly understood membrane proton transport phenomena to being recognized as ubiquitous proton pumps that underlie vital cellular processes in all eukaryotic and many prokaryotic cells. These exquisitely complex molecular motors also engage in diverse specialized roles contributing to development, tissue function and pH homeostasis within complex organisms. Increasingly, mutations and misappropriation of V-ATPase function have been linked to diseases, ranging from sclerosing bone pathologies and renal tubular acidosis to bone-loss disorders and cancer metastasis. Much remains to be learned about the details of V-ATPase cell and molecular biology; nevertheless, interest in V-ATPases as potential therapeutic targets has burgeoned in recent years. In this review, we present a history of our involvement and contributions to the understanding of V-ATPase structure and function and our nascent and ongoing contributions to translating the knowledge gained from basic research on the nature of V-ATPases into tools for drug discovery. We focus here primarily on the treatment of bone-loss pathologies, like osteoporosis, and present proof-of-concept for a drug screening strategy based on targeting a3-B2 subunit interactions within the V-ATPase complex.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".