Conserved and nonconserved features of the folding pathway of hisactophilin, a β‐trefoil protein
Bibliographic record
Abstract
Based on previous studies of interleukin-1beta (IL-1beta) and both acidic and basic fibroblast growth factors (FGFs), it has been suggested that the folding of beta-trefoil proteins is intrinsically slow and may occur via the formation of essential intermediates. Using optical and NMR-detected quenched-flow hydrogen/deuterium exchange methods, we have measured the folding kinetics of hisactophilin, another beta-trefoil protein that has < 10% sequence identity and unrelated function to IL-1beta and FGFs. We find that hisactophilin can fold rapidly and with apparently two-state kinetics, except under the most stabilizing conditions investigated where there is evidence for formation of a folding intermediate. The hisactophilin intermediate has significant structural similarities to the IL-1beta intermediate that has been observed experimentally and predicted theoretically using a simple, topology-based folding model; however, it appears to be different from the folding intermediate observed experimentally for acidic FGF. For hisactophilin and acidic FGF, intermediates are much less prominent during folding than for IL-1beta. Considering the structures of the different beta-trefoil proteins, it appears that differences in nonconserved loops and hydrophobic interactions may play an important role in differential stabilization of the intermediates for these proteins.
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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".