Author response: Super Spy variants implicate flexibility in chaperone action
Bibliographic record
Abstract
Proteins are made from long chains of smaller molecules, called amino acids, that twist and fold into complex three-dimensional shapes. Folding into the correct shape is crucial for a protein to function properly because many proteins work by binding to certain other proteins or molecules, like a key fitting into a lock. Additional proteins called chaperones often help with this folding process, and it has been proposed that chaperones must be particularly flexible in order to cope with the changes in the shape of the different proteins being folded. However, studying this hypothesis directly has proven to be difficult. Now, Quan et al. have tackled this challenge by using a bacterial assay—that they had developed previously—and which links the correct folding of a test protein to cell survival and growth in the presence of an antibiotic. This approach was formerly used to identify a new chaperone called Spy, and Quan et al. have now used it to find variants of this protein that perform as even better chaperones. This assay identified several variants of Spy that could stabilise an unstable test protein even more effectively than the wild-type Spy can. All of these variants were also better than the wild-type Spy at stabilising two other unfolded proteins—and so were dubbed ‘super Spy’ proteins. The mutations in the super Spy variants altered a region on the surface of Spy, which additional experiments revealed was likely to be involved in binding to the partner proteins. Furthermore, prior to binding to these partner proteins, the super Spy variants appear more flexible than the wild-type Spy protein. Quan et al. suggest that this increase in flexibility allows the super Spy variants to bind more tightly to a range of substrates, thus optimising their chaperone function.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.203 | 0.082 |
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".