Squaring the Circle in Peptide Assembly: From Fibers to Discrete Nanostructures by <i>de Novo</i> Design
Bibliographic record
Abstract
The design of bioinspired nanostructures and materials of defined size and shape is challenging as it pushes our understanding of biomolecular assembly to its limits. In such endeavors, DNA is the current building block of choice because of its predictable and programmable self-assembly. The use of peptide- and protein-based systems, however, has potential advantages due to their more-varied chemistries, structures and functions, and the prospects for recombinant production through gene synthesis and expression. Here, we present the design and characterization of two complementary peptides programmed to form a parallel heterodimeric coiled coil, which we use as the building blocks for larger, supramolecular assemblies. To achieve the latter, the two peptides are joined via peptidic linkers of variable lengths to produce a range of assemblies, from flexible fibers of indefinite length, through large colloidal-scale assemblies, down to closed and discrete nanoscale objects of defined stoichiometry. We posit that the different modes of assembly reflect the interplay between steric constraints imposed by short linkers and the bulk of the helices, and entropic factors that favor the formation of many smaller objects as the linker length is increased. This approach, and the resulting linear and proteinogenic polypeptides, represents a new route for constructing complex peptide-based assemblies and biomaterials.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".