Vector Geometry Mapping: A Method to Characterize the Conformation of Helix-Loop-Helix Calcium-Binding Proteins
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 Ca 2+ 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 Ca 2+ -sensor proteins within this superfamily, the Ca 2+ -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 C α 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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".