Resolving the functions of peptidylprolyl isomerases: insights from the mutagenesis of the nuclear FKBP25 enzyme
Bibliographic record
Abstract
Peptidylprolyl isomerases have been implicated in chromatin regulation through their association with histones, chromatin-modifying enzymes and DNA-binding transcription factors. As with other post-translational modifications to proteins, a mechanistic understanding of the regulation of biological processes is fostered by loss-of-function studies both in vitro and in vivo. For peptidylprolyl isomerases, this can be accomplished with small-molecule inhibitors with high affinity for the isomerase active site or by mutation of amino acid residues that contribute to catalysis. In the present article, we review caveats to each of these approaches, and place emphasis on the thorough characterization of loss-of-function mutations in FKBPs (FK506-binding proteins). Using a case study of mutagenesis of the nuclear FKBP25 peptidylprolyl isomerase enzyme, we demonstrate that certain mutations generate a loss-of-function phenotype because they induce a complete loss of the FKBP domain fold, whereas other mutations are 'surgical' in that they ablate catalytic isomerase activity, while maintaining domain structure. Peptidylprolyl isomerases are thought to have both catalytic and non-catalytic functions, but differentiating between these mechanisms has proved to be challenging. The domain-destabilizing and surgical mutants described will facilitate the characterization of these two reported functions of peptidylprolyl isomerases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".