Identification of key residues involved in Si transport by the aquaglyceroporins
Bibliographic record
Abstract
We recently demonstrated that the aquaglyceroporins (AQGPs) could act as potent transporters for orthosilicic acid (H4SiO4). Although interesting, this finding raised the question of whether water and H4SiO4, the transportable form of Si, permeate AQGPs by interacting with the same region of the pore, especially in view of the difference in molecular radius between the two substrates. Here, our goal was to identify residues that endow the AQGPs with the ability to facilitate Si diffusion by examining the transport characteristics of mutants in which residues were interchanged between a water-permeable but Si-impermeable channel (aquaporin 1 [AQP1]) and a Si-permeable but water-impermeable channel (AQP10). Our results indicate that the composition of the arginine filter (XX/R), known to include three residues that play an important role in water transport, may also be involved in Si selectivity. Interchanging the identities of the nonarginine residues within this filter causes Si transport to increase by approximately sevenfold in AQP1 and to decrease by approximately threefold in AQP10, whereas water transport and channel expression remain unaffected. Our results further indicate that two additional residues in the AQP arginine filter may be involved in substrate selectivity: replacing one of the residues has a profound effect on water permeability, and replacing the other has a profound effect on Si permeability. This study has thus led to the identification of residues that could play a key role in Si transport by the AQGPs and shown that substrate selectivity is likely ensured by more than one checkpoint within or near the pore.
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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) | 0.000 | 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".