Decoding Collagen's Thermally Induced Unfolding and Refolding Pathways
Bibliographic record
Abstract
Collagen has been evolutionarily selected as the preferred building block of extracellular structures. Despite inherent thermal instability of individual proteins at body temperature, collagen manages to assemble into higher-order structures that provide mechanical support to tissues. Sequence features that enhance collagen stability have been deduced largely from studies of collagen-mimetic peptides, as the large sizes of collagens have precluded high-resolution studies of their structure. Thus, there is a need for new methods to analyze the structure and mechanics of native collagen proteins. In this study, we used AFM imaging to investigate the temperature response of collagen types I, III and particularly IV. We observed a time-dependent loss of folded structures upon exposure to body temperature, with structural destabilization along the collagenous domain reflected by a shorter overall contour length. We characterized the sequence-dependent bending stiffness profile of collagen IV as a function of temperature and identified a putative initiation site for thermally induced unfolding. Interchain disulfide bonds in collagen IV were shown to enhance thermal stability and serve as the primary nucleation sites for in vitro refolding. In contrast to the canonical C-to-N terminal folding direction, we found an interchain cystine knot to enable folding in the opposite direction. Multiple sequence alignments revealed that this cystine knot is evolutionarily conserved across metazoan phyla, highlighting its significance in the stabilization of early collagen IV structures. Our findings provide mechanistic insights into the unfolding and refolding pathway of collagen IV, providing valuable insight into how its heterogeneous sequence influences stability and mechanics.
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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".