Chemically Modified “Polar Patch” Mutants of Subtilisin in Peptide Synthesis with Remarkably Broad Substrate Acceptance: Designing Combinatorial Biocatalysts
Bibliographic record
Abstract
A significant enhancement of the applicability of the serine protease subtilisin Bacillus lentus (SBL) in peptide synthesis was achieved by using the strategy of combined site-directed mutagenesis and chemical modification to create chemically modified mutant (CMM) enzymes. The introduction of polar and/or homochiral auxiliary substituents, such as X=oxazolidinones, alkylammonium groups, and carbohydrates at position 166 at the base of the primary specificity S(1) pocket created SBL CMMs S166C-S-X with strikingly broad structural substrate specificities. These CMMs are capable of catalyzing the coupling reactions of not only L-amino acid esters but also D-amino acid esters as acyl donors with glycinamide to give the corresponding dipeptides in good yields. These powerful enzymes are also applicable to the coupling of L-amino acid acyl donors with alpha-branched acyl acceptor, L-alaninamide. Typical increases in isolated yields of dipeptides of 60-80 % over SBL-WT (e.g. 0 % yield of Z-D-Glu-GlyNH(2) using SBL-WT-->74 % using S166C-S-(CH(2))(2) NMe(3) (+)) demonstrate the remarkable synthetic utility of this "polar patch" strategy. Such wide-ranging systems displaying broadened and therefore similarly high, balanced yields of products (e.g. 91 % Z-L-Ala-GlyNH(2) and 86 % yield of Z-D-Ala-GlyNH(2) using S166C-S-(3R,4S)-indenooxazolidinone) may now allow the use of biocatalysts in parallel library synthesis.
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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 | 0.000 |
| 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".