Bipartite Design of a Self-Fibrillating Protein Copolymer with Nanopatterned Peptide Display Capabilities
Bibliographic record
Abstract
The development of biomatrices for technological and biomedical applications employs self-assembled scaffolds built from short peptidic motifs. However, biopolymers composed of protein domains would offer more varied molecular frames to introduce finer and more complex functionalities in bioreactive scaffolds using bottom-up approaches. Yet, the rules governing the three-dimensional organization of protein architectures in nature are complex and poorly understood. As a result, the synthetic fabrication of ordered protein association into polymers poses major challenges to bioengineering. We have now fabricated a self-assembling protein nanofiber with predictable morphologies and amenable to bottom-up customization, where features supporting function and assembly are spatially segregated. The design was inspired by the cross-linking of titin filaments by telethonin in the muscle sarcomere. The resulting fiber is a two-protein system that has nanopatterned peptide display capabilities as shown by the recruitment of functionalized gold nanoparticles at regular intervals of ∼ 5 nm, yielding a semiregular linear array over micrometers. This polymer promises the uncomplicated display of biologically active motifs to selectively bind and organize matter in the fine nanoscale. Further, its conceptual design has high potential for controlled plurifunctionalization.
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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) | 0.001 | 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".