Identification of a cell-active chikungunya virus nsP2 protease inhibitor using a covalent fragment-based screening approach
Bibliographic record
Abstract
Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that has been responsible for numerous large-scale outbreaks in the last twenty years. Currently, there are no FDA-approved therapeutics for any alphavirus infection. CHIKV nonstructural protein 2 (nsP2), which contains a cysteine protease domain, is essential for viral replication, making it an attractive target for a drug discovery campaign. Here, we optimized a CHIKV nsP2 protease (nsP2pro) biochemical assay for the screening of a 6,120-compound cysteine-directed covalent fragment library. Using a 50% inhibition threshold, we identified 153 hits (2.5% hit rate). In dose–response follow-up, RA-0002034, a covalent fragment that contains a vinyl sulfone warhead, inhibited CHIKV nsP2pro with an IC 50 of 58 ± 17 nM, and further analysis with time-dependent inhibition studies yielded a k inact /K I of 6.4 × 10 3 M −1 s −1 . LC-MS/MS analysis determined that RA-0002034 covalently modified the catalytic cysteine in a site-specific manner. Additionally, RA-0002034 showed no significant off-target reactivity in proteomic experiments or against a panel of cysteine proteases. In addition to the potent biochemical inhibition of CHIKV nsP2pro activity and exceptional selectivity, RA-0002034 was tested in cellular models of alphavirus infection and effectively inhibited viral replication of both CHIKV and related alphaviruses. This study highlights the identification and characterization of the chemical probe RA-0002034 as a promising hit compound from covalent fragment-based screening for development toward a CHIKV or pan-alphavirus therapeutic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".