Characterization of caspase-7 interaction with RNA
Bibliographic record
Abstract
Apoptosis is a regulated form of cell death essential to the removal of unwanted cells. At its core, a family of cysteine peptidases named caspases cleave key proteins allowing cell death to occur. To do so, each caspase catalytic pocket recognizes preferred amino acid sequences resulting in proteolysis, but some also use exosites to select and cleave important proteins efficaciously. Such exosites have been found in a few caspases, notably caspase-7 that has a lysine patch (K38KKK) that binds RNA, which acts as a bridge to RNA-binding proteins favoring proximity between the peptidase and its substrates resulting in swifter cleavage. Although caspase-7 interaction with RNA has been identified, in-depth characterization of this interaction is lacking. In this study, using in vitro cleavage assays, we determine that RNA concentration and length affect the cleavage of RNA-binding proteins. Additionally, using binding assays and RNA sequencing, we found that caspase-7 binds RNA molecules regardless of their type, sequence, or structure. Moreover, we demonstrate that the N-terminal peptide of caspase-7 reduces the affinity of the peptidase for RNA, which translates into slower cleavages of RNA-binding proteins. Finally, employing engineered heterodimers, we show that a caspase-7 dimer can use both exosites simultaneously to increase its affinity to RNA because a heterodimer with only one exosite has reduced affinity for RNA and cleavage efficacy. These findings shed light on a mechanism that furthers substrate recognition by caspases and provides potential insight into its regulation during apoptosis.
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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 | 0.001 |
| 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".