Assignment of Ile, Leu, and Val Methyl Correlations in Supra-Molecular Systems: An Application to Aspartate Transcarbamoylase
Bibliographic record
Abstract
A number of complementary approaches for the assignment of Ile, Leu, and Val methyl groups in Methyl-TROSY spectra of supra-molecular protein complexes are presented and compared. This includes the transfer of assignments from smaller fragments to the complex using a "divide-and-conquer" approach, assignment transfer via exchange spectroscopy, or, alternatively, generating assignments of the complex through the measurement of pseudocontact shifts, facilitated by the introduction of paramagnetic probes. The methodology is applied to the assignment of the regulatory chains in the 300 kDa enzyme aspartate transcarbamoylase, ATCase. The "divide-and-conquer" method that has proven to be very powerful in applications to other systems produced assignments for approximately 60% of the observed methyl groups in TROSY maps of ATCase. By contrast, the combination of all approaches led to assignments for 86% of the methyls, providing a large number of probes of structure and dynamics. The derived assignments were used to interpret chemical shift changes of ATCase upon titration with the nucleotide ATP. Large shift changes in the N-terminal tails of the regulatory chain provide the first evidence for structural perturbations in a region that is known to play a critical role on the effect of nucleotide binding on distal catalytic sites of this allosteric enzyme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".