The Interaction of Bio-Molecules with Lipid Membranes Studied by X-ray Diffraction
Bibliographic record
Abstract
Abstract For the past 100 years, X-ray diffraction has been a powerful and indispensable tool to study the structure of matter. The challenge when studying molecular ordering in biological materials is their inherent disorder and strong fluctuations, which often suppress the formation of Bragg peaks. In the case of membranes, X-rays can detect molecules inside and confined between membranes. In this article we review examples to highlight the capabilities and accomplishments of X-ray scattering for the determination of membrane structure. X-ray diffraction gives quantitative information about partitioning of a small molecule, ethanol, in lipid bilayers. By taking amyloid-β peptides as examples, it is demonstrated that the position of peptides in lipid membranes can be determined with high precision. Confinement between membranes can organize molecules, as is the case for a mono-nucleotide, adenosine monophosphate, and the resulting pattern might be important to understand the formation of short RNA strands. With new approaches and techniques, and the increasingly powerful and capable devices, X-ray diffraction will continue to be the work horse for the determination of molecular structure in biological materials.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".