Development and characterization of a eukaryotic expression system for human type II procollagen
Bibliographic record
Abstract
BACKGROUND: Triple helical collagens are the most abundant structural protein in vertebrates and are widely used as biomaterials for a variety of applications including drug delivery and cellular and tissue engineering. In these applications, the mechanics of this hierarchically structured protein play a key role, as does its chemical composition. To facilitate investigation into how gene mutations of collagen lead to disease as well as the rational development of tunable mechanical and chemical properties of this full-length protein, production of recombinant expressed protein is required. RESULTS: Here, we present a human type II procollagen expression system that produces full-length procollagen utilizing a previously characterized human fibrosarcoma cell line for production. The system exploits a non-covalently linked fluorescence readout for gene expression to facilitate screening of cell lines. Biochemical and biophysical characterization of the secreted, purified protein are used to demonstrate the proper formation and function of the protein. Assays to demonstrate fidelity include proteolytic digestion, mass spectrometric sequence and posttranslational composition analysis, circular dichroism spectroscopy, single-molecule stretching with optical tweezers, atomic-force microscopy imaging of fibril assembly, and transmission electron microscopy imaging of self-assembled fibrils. CONCLUSIONS: Using a mammalian expression system, we produced full-length recombinant human type II procollagen. The integrity of the collagen preparation was verified by various structural and degradation assays. This system provides a platform from which to explore new directions in collagen manipulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".