Protein β-Sheet Nucleation Is Driven by Local Modular Formation
Bibliographic record
Abstract
Despite its central role in the protein folding process, the specific mechanism(s) behind beta-sheet formation has yet to be determined. For example, whether the nucleation of beta-sheets, often containing strands separated in sequence by many residues, is local or not remains hotly debated. Here, we investigate the initial nucleation step of beta-sheet formation by performing an analysis of the smallest beta-sheets in a non-redundant dataset on the grounds that the smallest sheets, having undergone little growth after nucleation, will be enriched for nucleating characteristics. We find that the residue propensities are similar for small and large beta-sheets as are their interstrand pairing preferences, suggesting that nucleation is not primarily driven by specific residues or interacting pairs. Instead, an examination of the structural environments of the two-stranded sheets shows that virtually all of them are contained in single, compact structural modules, or when multiple modules are present, one or both of the chain termini are involved. We, therefore, find that beta-nucleation is a local phenomenon resulting either from sequential or topological proximity. We propose that beta-nucleation is a result of two opposite factors; that is, the relative rigidity of an associated folding module that holds two stretches of coil close together in topology coupled with sufficient chain flexibility that enables the stretches of coil to bring their backbones in close proximity. Our findings lend support to the hydrophobic zipper model of protein folding (Dill, K. A., Fiebig, K. M., and Chan, H. S. (1993) Proc. Natl. Acad. Sci. U.S.A. 90, 1942-1946). Implications for protein folding are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".