Large-scale modelling as a route to multiple surface comparisons of the CCP module family
Bibliographic record
Abstract
Numerous mammalian proteins are constructed from a limited repertoire of module-types. Proteins belonging to the regulators of complement activation family--crucial for ensuring a complement-mediated immune response is targeted against infectious agents--are composed solely of complement control protein (CCP) modules. In the current study, CCP module sequences were grouped to allow selection of the most appropriate experimentally determined structures to serve as templates in an automated large-scale structure modelling procedure. The resulting 135 individual CCP module models, valuable in their own right, are available at the online database http://www.bru.ed.ac.uk/~dinesh/ccp-db.html. Comparisons of surface properties within a particular family of modules should be more informative than sequence alignments alone. A comparison of surface electrostatic features was undertaken for the first 28 CCP modules of complement receptor type 1 (CR1). Assignments to clusters based on surface properties differ from assignments to clusters based on sequences. This observation might reflect adaptive evolution of surface-exposed residues involved in protein-protein interactions. This illustrative example of a multiple surface-comparison was indeed able to pinpoint functional sites in CR1.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".