Controlled Peptide Solvation in Portion-Mixing Libraries of FRET Peptides: Improved Specificity Determination for Dengue 2 Virus NS2B-NS3 Protease and Human Cathepsin S
Bibliographic record
Abstract
The solubility of peptides in aqueous buffers used for the enzyme assays is a common limitation for all peptide libraries. In principle, the more water-soluble peptides are, the more susceptible they will be to peptidase hydrolysis. We have demonstrated that this bias can be circumvented in a portion-mixing fluorescence resonance energy transfer (FRET) peptide library by introducing k (lysine in the D-form) in both termini of the peptides. This more solvated library and another one without the k were assayed using trypsin and chymotrypsin as standard peptidases with high selectivity for R and K and for hydrophobic F and Y, respectively. Significantly improved consistency of the information on substrate profiles was obtained from the solvated library. The influence of improved solvation on substrate specificity determination was successfully demonstrated by the difference in specificity observed between the two libraries employing the human cathepsin S (accepts acidic, basic, or neutral amino acids at P1 position) and Dengue 2 virus NS2B-NS3 protease (high specificity to the pair of basic amino acids K-R, R-R, or Q-R/K at P2-P1 positions). In conclusion, hydration of the peptides has a major influence on protease processing, and this bias can be reduced in bound peptide libraries, improving reliability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".