Kinetic comparison of all eleven viral polyprotein cleavage site processing events by SARS-CoV-2 main protease using a linked protein FRET platform
Bibliographic record
Abstract
The main protease (M pro ) remains an essential therapeutic target for COVID-19 post infection intervention given its critical role in processing the majority of viral proteins encoded by the genome of SARS-CoV-2. Upon viral entry, the +ssRNA genome is translated into two long polyproteins (pp1a or the frameshift-dependent pp1ab) containing all the non-structural proteins (nsps) required by the virus for immune modulation, replication, and ultimately, virion assembly. Included among these nsps is the cysteine protease M pro (nsp5) which self-excises from the polyprotein, dimerizes, then consecutively cleaves 11 of the 15 cut site junctions found between each nsp within the polyprotein. Many structures of M pro (often bound to various inhibitor or peptide inhibitors) have been detailed recently, including structures of M pro bound to each of the polyprotein cleavage sequences, showing that M pro can accommodate a wide range of targets within its active site. However, to date, kinetic characterization of the interaction of M pro with each of its native cut site sequences remains incomplete. Here we present a robust and cost-effective FRET based system that benefits from a more consistent presentation of the substrate that is also closer in organization to the native polyprotein environment compared to previously reported FRET systems that employ chemically modified peptides. Using this system, we were able to show that while each site maintains a similar Michaelis constant, the catalytic efficiency of M pro varies greatly between cut site sequences, suggesting a clear preference for the order of nsp processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".