Inducing Microscale Structural Order in Phage Nanofilament Hydrogels via Macromolecular Crowding with Globular Proteins
Bibliographic record
Abstract
Biological hydrogels play important physiological roles in the body. These hydrogels often contain ordered subdomains that provide mechanical toughness and other tissue-specific functionality. Filamentous bacteriophages are nanofilaments with a high aspect ratio that can self-assemble into liquid crystalline domains that could be designed to mimic ordered biological hydrogels and can thus find application in biomedical engineering. We have previously reported hydrogels of pure crosslinked liquid crystalline filamentous phage formed at very high concentrations exhibiting a tightly packed microstructure and high stiffness. In this work, we report a method for inducing self-assembly of filamentous phage into liquid crystalline hydrogels at concentrations that are several orders of magnitude below that of lyotropic liquid crystal formation, thus creating structural order, but a less densely packed hydrogel. Hybrid hydrogels of M13 phage and bovine serum albumin (0.25 w/v%) were formed and shown to adsorb up to 16 its weight in water. Neither component gelled on its own at the low concentrations used, suggesting synergistic action between the two components in forming the hydrogel. The hybrid hydrogels exhibited repetitive self-healing under physiological conditions and at room temperature, autofluorescence in three channels, and antibacterial activity towards Escherichia coli host cells. Furthermore, the hybrid hydrogels exhibited more than 2 higher ability to pack water compared to BSA-only hydrogels and 2 higher flexibility (lower compression modulus) compared to tightly packed M13-only hydrogels, suggesting that our method could be used to create hydrogels with tunable mechanical properties through the addition of globular proteins, while maintaining structural order at the microscale.
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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".