Structure of voltage-dependent anion channel-tethered bilayer lipid membranes determined using neutron reflectivity
Bibliographic record
Abstract
Neutron reflectivity (NR) has emerged as a powerful technique to study the structure and behavior of membrane proteins at planar lipid interfaces. Integral membrane proteins (IMPs) remain a significant challenge for NR owing to the difficulty of forming complete bilayers with sufficient protein density for scattering techniques. One strategy to achieve high protein density on a solid substrate is the capture of detergent-stabilized, affinity-tagged IMPs on a nitrilotriacetic acid (NTA)-functionalized self-assembled monolayer (SAM), followed by reconstitution into the lipids of interest. Such protein-tethered bilayer lipid membranes (ptBLMs) have the notable advantage of a uniform IMP orientation on the substrate. Here, NR is used to provide a structural characterization of the ptBLM process from formation of the SAM to capture of the detergent-stabilized IMP and lipid reconstitution. The mitochondrial outer-membrane voltage-dependent anion channel (VDAC), which controls the exchange of bioenergetic metabolites between mitochondria and the cytosol, was used as a model β-barrel IMP. Molecular dynamics simulations were used for comparison with the experimental results and to inform the parameters of the physical models describing the NR data. The detailed structure of the SAM is shown to depend on the density of the NTA chelating groups. The relative content of detergent and protein in surface-immobilized, detergent-stabilized VDAC is measured, while the reconstituted lipid bilayer is shown to be complete to within a few percent, using the known atomic structure of VDAC. Finally, excess lipid above the reconstituted bilayer, which is of consequence for more indirect structural and functional studies, is shown to be present.
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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 | 0.001 |
| 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".