A hierarchical order within protein structures underlies large separations between strands in β‐sheets
Bibliographic record
Abstract
Protein β-sheets often involve nonlocal interactions between parts of the polypeptide chain that are separated by hundreds of residues, raising the question of how these nonlocal contacts form. A recent study of the smallest β-sheets found that their formation was not driven by signals hidden in the primary sequence. Instead, the strands in these sheets were either local in sequence, or, when separated by large sequential distances, the intervening residues were found to fold into compact modules that anchored distant parts of the chain in close spatial proximity. Here, we examine larger β-sheets to investigate the extensibility of this principle. From an analysis of the β-sheets in a nonredundant protein dataset, we find that a highly ordered hierarchical relationship exists in the intervening structure between nonlocal β-strands. This observation is almost universal: virtually all β-sheets, no matter their complexity, appear to adopt an antiparallel model to manage the nonlocal aspects of their assembly, one where the chain, having left the vicinity of an unfinished β-sheet, retraces its steps via the same route to complete the initial sheet. Exceptions typically involve unstructured regions at chain termini. Moreover, an analysis of the residues involved in nonlocal crossstrand interactions did not produce any evidence of a signal hidden in the sequence that might direct long-range interactions. These results build on those reported for the smallest sheets, suggesting that sheet formation is either local in sequence or local in space following prior folding events that anchor disparate parts of the chain in close proximity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".