Harnessing the functional diversity of plant cystatins to design inhibitor variants highly active against herbivorous arthropod digestive proteases
Bibliographic record
Abstract
Abstract Protein engineering approaches have been proposed to improve the inhibitory properties of plant cystatins against herbivorous arthropod digestive proteases. These approaches typically involve the site-directed mutagenesis of functionally relevant amino acids, the production and selection of improved inhibitory variants by molecular phage display procedures, or the design of bi/multifunctional translational fusions integrating one or several cystatin inhibitory domains. Here, we propose a new approach where the function-related structural elements of a cystatin are substituted by the corresponding elements of an alternative cystatin. Cys protease inhibitory assays were first performed with 20 representative plant cystatins and model Cys proteases, including herbivorous arthropod digestive proteases, to appreciate the extent of functional variability among plant cystatin protein family members. The most, and less, potent of these cystatins were then used as ‘donors’ of structural elements to create hybrids of tomato cystatin SlCYS8 used as a model ‘recipient’ inhibitor. Our data confirm the wide variety of cystatin protease inhibitory profiles among plant taxa. They also demonstrate the usefulness of these proteins as a pool of discrete structural elements for the design of cystatin variants with improved potency against herbivorous pest digestive Cys 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) | 0.000 | 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) | 0.001 | 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".