The accuracy of NMR protein structures in the Protein Data Bank
Bibliographic record
Abstract
Abstract We recently described a method, ANSURR, for measuring the accuracy of NMR protein structures. It is based on comparing residue-specific measures of rigidity from backbone chemical shifts via the random coil index, and from structures. Here, we report the use of ANSURR to analyse NMR ensembles within the Protein Data Bank (PDB). NMR structures cover a wide range of accuracy, which improved over time until about 2005, since when accuracy has not improved. Most structures have accurate secondary structure, but are too floppy, particularly in loops. There is a need for more experimental restraints in loops. The best current accuracy measures are Ramachandran distribution and number of NOE restraints per residue. The precision of structure ensembles correlates with accuracy, as does the number of hydrogen bond restraints per residue. If a structure contains additional components (such as additional polypeptide chains or ligands), then their inclusion improves accuracy. Analysis of over 7000 PDB NMR ensembles is available via our website ansurr.com .
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".