Enzymatic Activity in Fractal Networks of Self-Assembling Peptides
Bibliographic record
Abstract
The tissue environment is exceptionally complex, with well-controlled biochemical communication occurring between similar and dissimilar cells as well as between these cells and local extracellular matrices (ECM). To build an artificial ECM that can directly affect regional cell populations, a designer system should allow for controlled degradation, molecular release, and reorganization as related to local cellular function. (RADA)4 self-assembling peptide (SAP) hydrogels are excellent candidates for precisely tuned ECMs, or nanoscaffolds, with several beneficial qualities. They are a class of materials with uncomplicated fabrication and potentially allow for a diverse set of release strategies for many types of bioactive ligands. Enzyme-induced degradation and release of peptide sequences, synthesized within the SAP for on-demand cell signaling, could prove impactful to a plethora of human health applications. However, the degradation products and their release kinetics from these nanoscaffolds may greatly affect the overall system. To address this, enzyme kinetics in self-assembled hydrogels were studied by tethering matrix metalloproteinase 2 (MMP-2) cleavable peptide substrates of differing activities to the C-terminus of (RADA)4. High and low activity sequences, GPQG+IASQ (CP1) and GPQG+PAGQ (CP2), were respectively chosen for tunable release. When incubated with 5 nM MMP-2, over 3 days, both CP1 and CP2 sequences showed product formation values of ∼32% and ∼9% of the original substrate, respectively. On-demand product formation was found to be dependent upon both SAP composition and enzyme concentrations and could be tuned over the course of several days and weeks. Despite the fact that the self-assembling peptides are not directly cleavable by MMP-2, the CP1 and CP2 nanoscaffold morphology was visibly degraded by the protease. This degradation yielded a lower fractal dimensions for the matrix and suggested clearance of these materials may be possible over time.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 teacher head, 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".