Improved SARS-CoV-2 main protease high-throughput screening assay using a 5-carboxyfluorescein substrate
Bibliographic record
Abstract
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a global threat to human health has highlighted the need for the development of novel therapies targeting current and emerging coronaviruses with pandemic potential. The coronavirus main protease (M pro , also called 3CL pro ) is a validated drug target against coronaviruses and has been heavily studied since the emergence of SARS-CoV-2 in late 2019. Here, we report the biophysical and enzymatic characterization of native M pro , then characterize the steady-state kinetics of several commonly used FRET substrates, fluorogenic substrates, and six of the 11 reported SARS-CoV-2 polyprotein cleavage sequences. We then assessed the suitability of these substrates for high-throughput screening. Guided by our assessment of these substrates, we developed an improved 5-carboxyfluorescein-based FRET substrate, which is better suited for high-throughput screening and is less susceptible to interference and false positives than existing substrates. This study provides a useful framework for the design of coronavirus M pro enzyme assays to facilitate the discovery and development of therapies targeting M pro .
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".