The ATCUN Domain as a Probe of Intermolecular Interactions: Application to Calmodulin−Peptide Complexes
Bibliographic record
Abstract
The utility of a three-residue Cu2+ binding motif (ATCUN domain) for studying intermolecular interactions is demonstrated. By comparing a set of 1H-15N correlation spectra recorded on complexes of calmodulin (CaM) and peptides with the ATCUN tag in the presence and absence of Cu2+ the two possible canonical binding orientations of the peptide can be rapidly distinguished. The methodology is confirmed with studies of complexes of CaM and peptides from myosin light chain kinase and CaM kinase kinase, for which high-resolution structures are available, and then applied to a complex with CaM kinase I for which structural data has not been obtained. The orientation of the CaM kinase I and myosin light chain kinase peptides are shown to be identical. In the case of a complex of CaM with a peptide for which structural information is not available, the present methodology, in combination with 1H-15N residual dipolar couplings measured on CaM, and the database of existing CaM-peptide structures, allows a homology model to be built rapidly and with confidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".