The Identification of Residues That Control Signal Peptidase Cleavage Fidelity and Substrate Specificity
Bibliographic record
Abstract
Signal peptidase, which removes signal peptides from preproteins, has a substrate specificity for small uncharged residues at -1 (P1) and small or larger aliphatic residues at the -3 (P3) position. Structures of the catalytic domain with a 5S-penem inhibitor and a lipopeptide inhibitor reveal candidate residues that make up the S1 and S3 pockets that bind the P1 and P3 specificity residues of the preprotein substrate. We have used site-directed mutagenesis, mass spectrometric analysis, and in vivo and in vitro activity assays as well as molecular modeling to examine the importance of the substrate pocket residues. Generally, we find that the S1 and S3 binding sites can tolerate changes that are expected to increase or decrease the size of the pocket without large effects on activity. One residue that contributes to the high fidelity of cleavage of signal peptidase is the Ile-144 residue. Changes of the Ile-144 residue to cysteine result in cleavage at multiple sites, as determined by mass spectrometry and Edman sequencing analysis. In addition, we find that signal peptidase is able to cleave after phenylalanine at the -1 residue in a double mutant in which both Ile-86 and Ile-144 were changed to an alanine. Also, alteration of the Ile-144 and Ile-86 residues to the corresponding residues found in the homologous Imp1 protease changes the specificity to promote cleavage following a -1 Asn residue. This work shows that Ile-144 and Ile-86 contribute to the signal peptidase substrate specificity and that Ile-144 is important for the accuracy of the cleavage reaction.
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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 | 0.001 |
| 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".