Functional and Structural Characterization of Treatment-Emergent Nirmatrelvir Resistance Mutations at Low Frequencies in the Main Protease (Mpro) Reveals a Unique Evolutionary Route for SARS-CoV-2 to Gain Resistance
Bibliographic record
Abstract
BACKGROUND: The main protease (Mpro) is one of the most attractive targets for antiviral drug discovery against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Mutations in Mpro have been linked to resistance against nirmatrelvir-ritonavir (NIR-RIT), an important therapy for SARS-CoV-2 infection. This study aimed to identify low-frequency antiviral resistance mutations in Mpro from NIR-RIT-treated patients and to analyze the enzymatic properties, inhibitor susceptibility, and structural features of new Mpro clinical variants. METHODS: We screened 1528 SARS-CoV-2-positive patients from 2 hospitals and identified 17 who remained positive after treatment. Whole-genome sequencing of nasopharyngeal specimens was conducted to identify Mpro clinical variants. The impact of these mutations on Mpro activity and inhibitor susceptibility was investigated using a fluorescent enzymatic biosensor in human cells, along with in vitro thermal stability and structure-based analyses of the Mpro mutants and Mpro-NIR complexes. RESULTS: The analysis identified 2 novel Mpro clinical variants: D48D/L58F/P132H (variant 1) and D48D/L67V/K90R/P132H (variant 2). Our data show that the selected clinical mutations are localized in the Mpro N-terminal domain, are far from the catalytic site, and strongly impact NIR resistance without affecting Mpro activity. Structural analysis and thermal denaturation analyses revealed that these mutations may disrupt the substrate binding site's structure and dynamics, reducing protein stability and potentially impacting substrate binding or dimerization without compromising catalytic activity. CONCLUSIONS: Our new Mpro clinical mutations that confer complete resistance to NIR were not identified during previous cell-culture-based studies. More research is needed to explore resistance mechanisms, providing insights into strategies that mitigate resistance and protect therapeutic efficacy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".