Proteomics as a tool to gain next level insights into photo-crosslinkable biopolymer modifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The distribution of photo-crosslinkable moieties onto a protein backbone can affect a biomaterial's crosslinking behavior, and therefore also its mechanical and biological properties. A profound insight in this respect is essential for biomaterials exploited in tissue engineering and regenerative medicine. In the present work, photo-crosslinkable moieties have been introduced on the primary amine groups of: (i) a recombinant collagen peptide (RCPhC1) with a known amino acid (AA) sequence, and (ii) bovine skin collagen (COL BS) with an unknown AA sequence. The degree of substitution (DS) was quantified with two conventional techniques: an ortho-phthalic dialdehyde (OPA) assay and 1H NMR spectroscopy. However, neither of both provides information on the exact type and location of the modified AAs. Therefore, for the first time, proteomic analysis was evaluated herein as a tool to identify functionalized AAs as well as the exact position of photo-crosslinkable moieties along the AA sequence, thereby enabling an in-depth, unprecedented characterization of functionalized photo-crosslinkable biopolymers. Moreover, our strategy enabled to visualize the spatial distribution of the modifications within the overall structure of the protein. Proteomics has proven to provide unprecedented insight in the distribution of photo-crosslinkable moieties along the protein backbone, undoubtedly contributing to superior functional biomaterial design to serve regenerative medicine.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it