Determination of Peptide Substrate Specificity for μ-Calpain by a Peptide Library-based Approach
Bibliographic record
Abstract
Calpains are proteases that catalyze the limited cleavage of target proteins in response to Ca(2+) signaling. Because of their involvement in pathological conditions such as post-ischemic injury and Alzheimer and Parkinson disease, calpains form a class of pharmacologically significant targets for inhibition. We have determined the sequence preference for the hydrolysis of peptide substrates of the ubiquitous mu-calpain isoform by a peptide library-based approach using the proteolytic core of mu-calpain (muI-II). The approach, first described by Turk et al. (Turk, B. E., Huang, L. L., Piro, E. T., and Cantley, L. C. (2001) Nat. Biotechnol. 19, 661-667), involved the digestion of an N-terminally acetylated degenerate peptide library in conjunction with Edman sequencing to determine the specificity for residues found at primed positions. The cleavage consensus for these positions was then used to design a second, partially degenerate library, to determine specificity at unprimed positions. We have improved upon the original methodology by using a degenerate peptide dendrimer for determination of specificity at unprimed positions. By using this modified approach, the complete cleavage specificity profile for muI-II was determined for all positions flanking the cleaved peptide. A previously known preference of calpains for hydrophobic amino acids at unprimed positions was confirmed. In addition, a novel residue specificity for primed positions was revealed to highlight the importance of these sites for substrate recognition. The optimal primed site motif (MER) was shown to be capable of directing cleavage to a specific peptide bond. Accordingly, we designed a fluorescent resonance energy transfer-based substrate with optimal cleavage motifs on the primed and non-primed sides (PLFAER). The mu-calpain core shows a far greater turnover rate for our substrate than for those based on the cleavage site of alpha-spectrin or the proteolytic sequence consensus compiled from substrate alignments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".