A case study on the treatment of protein SIRAS data
Bibliographic record
Abstract
A case study has been made on the treatment of the SIRAS (single isomorphous replacement with anomalous scattering) data of the originally unknown protein LegC3N. An alternative treatment has been proposed which led to improved results in this particular test case. The treatment involves iterative direct-method SAD (single-wavelength anomalous diffraction) phasing and direct-method-aided model completion, both of which are implanted in the IPCAS (Iterative Protein Crystal-structure Automatic Solution) pipeline. Apart from the experimental data, a simulated SIRAS data set for LegC3N with the derivative data truncated to 5.0 Å resolution has also been tested. SAD phasing and phase/model extension in PHENIX without direct methods failed to solve the structure using these simulated SIRAS data. However, the procedure proposed here involving direct methods in both SAD phasing and phase/model extension led to a nearly complete structure model. This shows the potential ability of treating SIRAS data with a derivative diffracting to lower resolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".