Identification of naturally occurring drug-resistant mutations of SARS-CoV-2 papain-like protease
Nature Communications·
- DOI
- 10.1038/s41467-025-59922-9
- PMID
- 40379662
- PMCID
- PMC12084387
- OpenAlex
- W4410386613
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The findings characterize resistance-relevant hotspots in PL pro that may inform the design of next-generation inhibitors, with potential relevance to antiviral preparedness against SARS-CoV-2.
Structured evidence summary
Research question
The study investigates whether naturally occurring mutations at the drug-binding site of the SARS-CoV-2 papain-like protease (PL pro) confer resistance to first-in-class PL pro inhibitors such as Jun12682 and PF-07957472.
Study design
A laboratory-based molecular characterization study combining enzymatic assays of PL pro mutants, independent serial viral passage experiments, and computational analyses including molecular dynamics simulations and perturbative free energy calculations.
Population and setting
In vitro enzymatic systems, viral passage experiments (model context), and in silico molecular simulations; no human or clinical population is described.
Main findings
Several naturally occurring PL pro mutants at the drug-binding site showed significant resistance to Jun12682 while retaining enzymatic activity comparable to wild-type. Mutants E167G and Q269H were validated as physiologically relevant through serial viral passage. Computational analyses indicated resistance was associated with weakened hydrogen bonding and π-π stacking interactions. Residues E167, Y268, and Q269 were identified as drug-resistant hotspots in the BL2 loop and groove region.
Public-health relevance
The findings characterize resistance-relevant hotspots in PL pro that may inform the design of next-generation inhibitors, with potential relevance to antiviral preparedness against SARS-CoV-2.
Important limitations
The abstract does not state explicit limitations. This summary is limited to the supplied single-article abstract and metadata and requires the original paper for decision-grade interpretation. Findings are based on in vitro enzymatic assays, viral passage models, and computational simulations, and clinical or population-level resistance relevance is not demonstrated within the supplied evidence.
GIDS interpretation
The paper is discoverable through the GIDS classifier under the topics of SARS-CoV-2/COVID-19 and antimicrobial resistance, providing context for indexing rather than establishing a connection to any live surveillance signal.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 7 auditable classifier relationships to diseases, places, topics, and study design.