Vector Geometry Mapping: A Method to Characterize the Conformation of Helix-Loop-Helix Calcium-Binding Proteins
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Bibliographic record
Abstract
Members of the EF-hand protein superfamily (1) share a common calciumbinding helix-loop-helix motif as a building block, whose conformation essentially determines biological function. It has been well demonstrated that specific binding of Ca2+to the loop alters conformation of the motif, involving rearrangement of the two helices of the EF-hand in three-dimensional (3-D) space (reviewed in refs. 2–4. In Ca2+-sensor proteins within this superfamily, the Ca2+-induced conformational change is responsible for the sensor activity (2). For many years this change has been quantitatively characterized by the interhelical angle measured between the two helices (5–9). Recently, Nelson and Chazin (10) reported an interaction-based analysis for examining conformational change in EF-hand proteins, including computation of distance difference matrices (calculated between each pair of Cα atoms in two structures). Both methods have advantages and disadvantages. The former approach gives a single, descriptive parameter for a given EF-hand, but is obviously insufficient to describe the conformation and its change in detail. The latter approach is more comprehensive and is sensitive to small conformational changes, but yields a large number of parameters to be interpreted by the user. In this chapter, we describe a method termed Vector Geometry Mapping (VGM), an extension of the “interhelical angle”approach, which produces amore complete and descriptive picture of EF-hand conformations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it