Introduction and validation of an invariom database for amino-acid, peptide and protein molecules
Bibliographic record
Abstract
A database of invarioms for structural refinement of amino-acid, oligopeptide and protein molecules is presented. The spherical scattering factors of the independent atom or promolecule model are replaced by ;individual' aspherical scattering factors that take into account the chemical environment of a bonded atom. All amino acids were analysed in terms of their invariom fragments. In order to generate 73 database entries that cover this class of compounds, 37 model compounds were geometry-optimized and theoretical structure factors were calculated. Multipole refinements were then performed on these theoretical structure factors to yield the invariom database. Validation of this database on an extensive number of experimental small-molecule crystal structures of varying quality and resolution shows that invariom modelling improves various figures of merit. Differences in figures of merit between invariom and promolecule models give insight into the importance of disorder for future protein-invariom refinements. The suitability of structural data for application of invarioms can be predicted by Cruickshank's diffraction-component precision index [Cruickshank (1999), Acta Cryst. D55, 583-601].
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".