Mechanistic Insights into Cyclodipeptide Formation by Cyclodipeptide Synthases: A Preliminary Exploration on Pathways and Catalytic Residues
Bibliographic record
Abstract
Cyclodipeptide synthases (CDPSs) are enzymes that synthesize cyclodipeptides using two aminoacyl-tRNAs as substrates, but their mechanism remains unclear. This study aims to elucidate the mechanism of AlbC, a CDPS that produces cyclo(L-Phe-L-Phe). We employed small-model quantum mechanics (QM) calculations to propose an intrinsic pathway and molecular dynamics (MD) simulations to identify key catalytic residues involved in this process. The mechanism involves three main steps: activation of Ser37 and the first tRNAPhe to form a Phe-enzyme intermediate, binding of the second tRNAPhe to form a dipeptidyl enzyme intermediate, and intramolecular cyclization to yield the cyclodipeptide. Our QM calculations suggest that Ser37 can be activated through direct transfer of its hydroxyl proton to the O3’ atom of the first substrate. MD simulations highlight the roles of Gly35, Asn40, and His203 in stabilizing the Phe-enzyme intermediate, thus lowering the calculated intrinsic barrier. In the second step, the dipeptidyl enzyme intermediate is favored over the nucleoside intermediate and is stabilized by Asn40, Gln182, and His203 in the AlbC active site, where Gln182 may act as a catalytic base. Additionally, Asn159 and His203 contribute to lowering the significant energy barrier observed in QM calculations for intramolecular cyclization, with Glu182 potentially serving as a catalytic base during this process. Overall, our results support the roles of Asn40 and His203 throughout all mechanistic steps, while highlighting Glu182's involvement in the formation of the dipeptidyl enzyme intermediate and intramolecular cyclization steps. These insights can guide future enzymatic modeling studies of AlbC and potentially other CDPS enzymes using similar approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".