Inhibitor‐induced dimerization mediates lufotrelvir resistance in mutants of <scp>SARS</scp>‐<scp>CoV</scp>‐2 <scp>3C‐like protease</scp>
Bibliographic record
Abstract
Abstract The emergence of SARS‐CoV‐2 and other lethal coronaviruses has prompted extensive research into targeted antiviral treatments, particularly focusing on the viral 3C‐like protease (3CLpro) due to its essential role for viral replication. However, the rise of drug resistance mutations poses threats to public health and underscores the need to predict resistance mutations and understand the mechanism of how these mutations confer resistance. The binding of inhibitor to 3CLpro drives it from the monomeric to the active dimeric form, which can counterintuitively lead to enzyme activation rather than inhibition. Furthermore, we find this allosteric coupling between binding and dimerization is sensitive to mutation, leading to a new mechanism for drug resistance. Understanding the relationship between inhibitor binding and dimerization is important for resistant strain surveillance and development of robust antivirals. Herein, we present a systematic study of drug resistance mediated by inhibitor‐induced dimerization of 3CLpro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 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".