A method for validating the accuracy of NMR protein structures
Bibliographic record
Abstract
Abstract We present a method, Accuracy of NMR Structures using Random Coil Index and Rigidity (ANSURR), that measures the accuracy of NMR protein structures. It provides a residue-by-residue comparison of two measures of local rigidity: the Random Coil Index [RCI] (a measure of the extent to which backbone chemical shifts adopt random coil values); and local rigidity predicted by mathematical rigidity theory using the computational method Floppy Inclusion and Rigid Substructure Topology [FIRST], calculated from an NMR structural model. We compare RCI and FIRST using a correlation score (which assesses the location of secondary structure), and an RMSD score (which measures overall rigidity, and mainly assesses hydrogen bond correctness). We test the performance of ANSURR using: (a) structures refined in explicit solvent, which have much better RMSD score than unrefined structures, though similar correlation; (b) decoy structures generated for 89 NMR structures. The experimental NMR structures are usually better, though helical and sheet structures behave differently; (c) conventional predictors of structural accuracy such as number of restraints per residue, restraint violations, energy of structure, RMSD of the ensemble (precision of the calculation), Ramachandran distribution, and clashscore. Comparisons of NMR to crystal structures show that secondary structure is equally accurate in both, but crystal structures tend to be too rigid in loops, whereas NMR structures tend to be too floppy overall.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".