A robust fluorogenic substrate for chikungunya virus protease ( <scp>nsP2</scp> ) activity
Bibliographic record
Abstract
Abstract Chikungunya virus (CHIKV) is an emerging pathogen with pandemic potential. CHIKV infection in humans is transmitted by mosquitoes and induces common symptoms of high fever, arthralgia and myalgia. Because no specific antiviral drugs for treatment of CHIKV infection are available, drug development remains a central goal. The chikungunya virus protease from nsP2 (CHIKVP) has emerged as a key drug target due to its indispensable role in viral replication via cleavage of the viral polyprotein. To date, effective tools for screening for CHIKVP inhibitors that reflect the most critical polyprotein cleavage sites have been lacking, hampering drug‐development efforts. We found that the recognition ability of CHIKVP is sensitive to the length of peptide substrates. In this study, we report a robust fluorogenic substrate comprising a 15‐mer peptide derived from the nsP3/4 junction from the CHIKV polyprotein. This peptide is flanked by an ACC‐Lys(dnp) donor–quencher pair. Our new substrate acc‐CHIK 15 ‐dnp shows a 30‐fold improved signal‐to‐noise ratio as compared to the previously reported edab 8 substrate, which is also based on the nsP3/4 junction. We found acc‐CHIK 15 ‐dnp is recognized only by CHIKVP but not by other alphavirus proteases. This is surprising due to the high level of sequence conservation in the alpha virus polyprotein junctions and indicates that the P‐side residues are more important than the P′‐side sequence for effective CHIKVP cleavage. The robust signal‐to‐noise ratio obtained using acc‐CHIK 15 ‐dnp derived from the nsP3/4 cleavage site enabled much improved small molecule HTS on CHIKV relative to other fluorogenic reporters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".