Gold nanoparticles partition to and increase the activity of glucose-6-phosphatase in a synthetic phospholipid membrane system
Bibliographic record
Abstract
Engineered nanomaterials can alter the structure and/or function of biological membranes and membrane proteins but the underlying mechanisms remain unclear. We addressed this using a Langmuir phospholipid monolayer containing an active transmembrane protein, glucose-6-phosphatase (G6Pase). Gold nanoparticles (nAu) with varying ligand shell composition and hydrophobicity were synthesized, and their partitioning in the membrane and effects on protein activity characterized. nAu incorporation did not alter the macroscopic properties of the membrane. Atomic force microscopy showed that when co-spread with other components prior to membrane compression, nAu preferentially interacted with G6Pase and each other in a functional group-dependent manner. Under these conditions, all nAu formulations reduced G6Pase aggregation in the membrane, enhancing catalytic activity 5-6 fold. When injected into the subphase beneath pre-compressed monolayers, nAu did not affect G6Pase activity over 60 minutes, implying they were unable to interact with the protein under these conditions. A small but significant quenching of tryptophan fluorescence showed that nAu interacted with G6Pase in aqueous suspension. nAu also significantly reduced the hydrodynamic diameter of G6Pase in aqueous suspension and promoted catalytic activity, likely via a similar mechanism to that observed in co-spread monolayers. Overall, our results show that nAu can incorporate into membranes and associate preferentially with membrane proteins under certain conditions and that partitioning is dependent upon ligand shell chemistry and composition. Once incorporated, nAu can alter the distribution of membrane proteins and indirectly affect their function by improving active site accessibility, or potentially by changing their native structure and distribution in the membrane.
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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".