Modelling the structure of the red cell membraneThis paper is one of a selection of papers published in a Special Issue entitled CSBMCB 53rd Annual Meeting — Membrane Proteins in Health and Disease, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
The red cell membrane has long been the focus of extensive study. The macromolecules embedded within the membrane carry the blood group antigens and perform many functions including the vital task of gas exchange. Links between the intramembrane macromolecules and the underlying cytoskeleton stabilize the biconcave morphology of the red cell and allow deformation during microvascular transit. Much is now known about the proteins of the red cell membrane and how they are organised. In many cases we have an understanding of which proteins are expressed, the number of each protein per cell, their oligomeric state(s), and how they are collected in large multi-protein complexes. However, our typical view of these structures is as cartoon shapes in schematic figures. In this study we have combined knowledge of the red cell membrane with a wealth of protein structure data from crystallography, NMR, and homology modelling to generate the first, tentative models of the complexes which link the membrane to the cytoskeleton. Measurement of the size of these complexes and comparison with known cytoskeletal distance parameters suggests the idea of interaction between the membrane complexes, which may have profound implications for understanding red cell function and deformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".