Designing Catalysts to Accelerate a Protein–Peptide Assembly-Reaction Cascade
Bibliographic record
Abstract
A biological system is rich in dynamic biomolecular assembly reaction cascades mediated by enzymes and molecular chaperones, as represented by the formation of the "chain-mail-like" bacteriophage HK97 capsid that involves sequential events of chaperone-assisted assembly and cross-linking reactions. To shed light on such catalyzed assembly processes, we report an artificial protein-peptide "assembly-reaction" cascade that can be accelerated by rationally designed catalysts. The cascade is inhibited by a tethered SpyTag mutant that blocks SpyCatcher from the subsequent reactions. A designed fusion of calmodulin and sortase can promote the cascade by first binding with M13 at the loop between the SpyTag mutant and SpyCatcher to open the gating via a coil-helix transition. After the SpyTag-SpyCatcher reaction, the catalyst is regenerated by sortase-mediated cyclization that restores the constrained M13 conformation at the loop to release the bounded calmodulin. In the presence of 0.1 equiv of catalyst, the process can be accelerated, increasing the initial rate by ∼12-fold and reducing the half-life by ∼17-fold. With experimentally measured kinetic parameters, we simulated this system through microkinetic modeling, illustrated the contributions of each parameter, and proposed conditions for optimal catalytic performance. As a prototype of artificial catalyzed supramolecular "assembly-reaction" cascades, this work is reminiscent of those catalyzed cascades in nature. Their common features reveal similar underlying physiochemical principles and suggest new avenues to understand and interfere with biological systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".