Hidden potential of the supporting scaffold as a structural module for plant cystatin design
Bibliographic record
Abstract
Abstract Protein engineering approaches have been proposed to improve the inhibitory properties of plant cystatins towards herbivorous pest digestive Cys proteases, typically involving sequence alterations in the inhibitory loops and/or N-terminal trunk of the protein interacting with specific amino acid residues of the target protease. In this study, we assessed whether the loops-supporting frame, or scaffold, would represent a valuable structural module for cystatin function improvement. Twenty hybrid cystatins were designed in silico , consisting of the N-terminal trunk and two inhibitory loops of a given donor cystatin grafted onto the scaffold of an alternative, recipient cystatin. Synthetic genes for the hybrids were expressed in E. coli , and the resulting proteins assessed for their potency to inhibit model Cys protease papain and the digestive Cys proteases of Colorado potato beetle ( Leptinotarsa decemlineata ) used as an insect pest model. In line with the occurrence of positively selected amino acids presumably influencing inhibitory activity in the scaffold region of plant cystatins, grafting the N-terminal trunk and inhibitory loops of a given cystatin onto the scaffold of an alternative cystatin generally had an effect on the inhibitory potency of these function-related elements against Cys proteases. For instance, hybrid cystatins including the three structural elements of model tomato cystatin SlCYS8 grafted on the scaffold of cystatins from other plant families showed K i values altered by up to 3-fold for papain, and inhibitory efficiencies increased by up to 8-fold against L. decemlineata cathepsin L-like proteases, compared to wild-type SlCYS8 bearing the original scaffold. Our data point to a significant influence of the cystatin scaffold on the inhibitory activity of the N-terminal trunk and protease inhibitory loops. They also suggest the potential of this structural element as a module for plant cystatin design to generate functional variability against Cys proteases, including the digestive proteases of herbivorous pests.
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) | 0.000 | 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".