Toward Clickable Protein Networks: Orthogonal Amidation of Self-Assembled Lysozyme and Bovine Serum Albumin Nanofibers
Bibliographic record
Abstract
Self-assembled protein nanofibers (PNFs) are promising building blocks for the development of sustainable materials due to their functional versatility, inherent biodegradability, and thermodynamic stability. In order to broaden the applications of PNFs, chemical modification offers a simple way to incorporate specific functionalization throughout the PNF fiber backbone. To this end, we demonstrate a highly efficient amidation of self-assembled PNFs from bovine serum albumin (BSA) and hen egg white lysozyme (HEWL), using adipic acid dihydrazide (ADH) and aminoacetaldehyde dimethyl acetal (AADA) as bioorthogonal modifiers, offering the possibility to form covalent networks via click chemistry. Critically, we compare (dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride (DMTMM)-mediated amidations with the widely used N -(3-(dimethylamino)propyl)- N ′-ethylcarbodiimide hydrochloride and N -hydroxysuccimine (EDC/NHS) mediation system, showcasing superior reaction efficiency and reduced pH dependency in the case of DMTMM. Importantly, the PNF backbone remained largely intact, albeit some shortening of the fibers was evidenced. As a proof of concept, aldehyde-functionalized HEWL PNFs and hydrazide-functionalized BSA PNFs were mixed together to demonstrate kinetically bioorthogonal hydrazone cross-linking, relevant for many biomedical applications. Overall, this work provides an efficient and simple approach for modifying PNFs, which could find use in applications ranging from biomedicine (drug delivery or tissue engineering) to cosmetics, food production. and agriculture.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".