Protein-Protein Interactions in Membranes
Bibliographic record
Abstract
In this article we review the current status of our understanding of membrane mediated interactions from theory and experiment. Phenomenological mean field and molecular models will be discussed and compared to recent experimental results from dynamical neutron scattering and atomic force microscopy. Keywords: Protein-protein interactions, membrane inclusions, lipid mediated interactions, membrane theory, membrane dynamics, neutron scattering, membrane mediated interactions, Phenomenological, dynamical neutron scattering, atomic force microscopy, Analytical Theories, Computer Supported Theories, Lateral Coupling, Cooperative Dynamics, Native Membrane Protein Bacteriorhodopsin, bilayer and monolayers, hydrophobic membrane, metastable state, Purple membrane, AFM, protein-distributed state, lipid-hydrocarbon density, phenomenological models, interfacial tension, hydrophobic amphiphile tailsProtein-protein interactions, membrane inclusions, lipid mediated interactions, membrane theory, membrane dynamics, neutron scattering, membrane mediated interactions, Phenomenological, dynamical neutron scattering, atomic force microscopy, Analytical Theories, Computer Supported Theories, Lateral Coupling, Cooperative Dynamics, Native Membrane Protein Bacteriorhodopsin, bilayer and monolayers, hydrophobic membrane, metastable state, Purple membrane, AFM, protein-distributed state, lipid-hydrocarbon density, phenomenological models, interfacial tension, hydrophobic amphiphile tails
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".