Altered -3 Substrate Specificity of Escherichia coli Signal Peptidase 1 Mutants as Revealed by Screening a Combinatorial Peptide Library
Bibliographic record
Abstract
Signal peptidase functions to cleave signal peptides from preproteins at the cell membrane. It has a substrate specificity for small uncharged residues at -1 (P1) and aliphatic residues at the -3 (P3) position. Previously, we have reported that certain alterations of the Ile-144 and Ile-86 residues in Escherichia coli signal peptidase I (SPase) can change the specificity such that signal peptidase is able to cleave pro-OmpA nuclease A in vitro after phenylalanine or asparagine residues at the -1 position (Karla, A., Lively, M. O., Paetzel, M. and Dalbey, R. (2005) J. Biol. Chem. 280, 6731-6741). In this study, screening of a fluorescence resonance energy transfer-based peptide library revealed that the I144A, I144C, and I144C/I86T SPase mutants have a more relaxed substrate specificity at the -3 position, in comparison to the wild-type SPase. The double mutant tolerated arginine, glutamine, and tyrosine residues at the -3 position of the substrate. The altered specificity of the I144C/I86T mutant was confirmed by in vivo processing of pre-beta-lactamase containing non-canonical arginine and glutamine residues at the -3 position. This work establishes Ile-144 and Ile-86 as key P3 substrate specificity determinants for signal peptidase I and demonstrates the power of the fluorescence resonance energy transfer-based peptide library approach in defining the substrate specificity of proteases.
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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) | 0.000 | 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 | 0.000 |
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".