A statistically derived parameterization for the collagen triple‐helix
Bibliographic record
Abstract
The triple-helix is a unique secondary structural motif found primarily within the collagens. In collagen, it is a homo- or hetero-tripeptide with a repeating primary sequence of (Gly-X-Y)(n), displaying characteristic peptide backbone dihedral angles. Studies of bulk collagen fibrils indicate that the triple-helix must be a highly repetitive secondary structure, with very specific constraints. Primary sequence analysis shows that most collagen molecules are primarily triple-helical; however, no high-resolution structure of any entire protein is yet available. Given the drastic morphological differences in self-assembled collagen structures with subtle changes in assembly conditions, a detailed knowledge of the relative locations of charged and sterically bulky residues in collagen is desirable. Its repetitive primary sequence and highly conserved secondary structure make collagen, and the triple-helix in general, an ideal candidate for a general parameterization for prediction of residue locations and for the use of a helical wheel in the prediction of residue orientation. Herein, a statistical analysis of the currently available high-resolution X-ray crystal structures of model triple-helical peptides is performed to produce an experimentally based parameter set for predicting peptide backbone and C(beta) atom locations for the triple-helix. Unlike existing homology models, this allows easy prediction of an entire triple-helix structure based on all existing high-resolution triple-helix structures, rather than only on a single structure or on idealized parameters. Furthermore, regional differences based on the helical propensity of residues may be readily incorporated. The parameter set is validated in terms of the predicted bond lengths, backbone dihedral angles, and interchain hydrogen bonding.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".