Contributions of hyperactive mutations in Mpro from SARS-CoV-2 to drug resistance
Flynn, J. M.; Zvornicanin, S. N.; Shaqra, A. M.; Kurt Yilmaz, N.; Moquin, S.; Dovala, D.; Schiffer, C. A.; Bolon, D.
Show abstract
The appearance and spread of mutations that cause drug resistance in rapidly evolving diseases, including infections by SARS-CoV-2 virus, are major concerns for human health. Many drugs target enzymes, and resistance-conferring mutations impact inhibitor binding and/or enzyme activity. Nirmatrelvir, the most widely used inhibitor currently used to treat SARS-CoV-2 infections, targets the main protease (Mpro) preventing it from processing the viral polyprotein into active subunits. Our previous work systematically analyzed resistance mutations in Mpro that reduce binding to inhibitors; here we investigate mutations that affect enzyme function. Hyperactive mutations that increase Mpro activity can contribute to drug resistance but had not been thoroughly studied. To explore how hyperactive mutations contribute to resistance, we comprehensively assessed how all possible individual mutations in Mpro affect enzyme function using a mutational scanning approach with a FRET-based yeast readout. We identified hundreds of mutations that significantly increased Mpro activity. Hyperactive mutations occurred both proximal and distal to the active site, consistent with protein stability and/or dynamics impacting activity. Hyperactive mutations were observed three times more than mutations which reduced apparent binding to nirmatrelvir in recent studies of laboratory grown viruses selected for drug resistance. Hyperactive mutations were also about three times more prevalent than nirmatrelvir-binding mutations in sequenced isolates from circulating SARS-CoV-2. Our findings indicate that hyperactive mutations are likely to contribute to the natural evolution of drug resistance in Mpro and provide a comprehensive list for future surveillance efforts.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Development of a new DHFR-based destabilizing domain with enhanced basal turnover and applicability in mammalian systems 95%
- Molecular and structural mechanism of pan-genotypic HCV NS3/4A protease inhibition by glecaprevir 95%
- Rationalizing diverse binding mechanisms to the same protein fold: in-sights for ligand recognition and biosensor design 93%
Similar papers in this journal
- Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning 95%
- Structure-function Analyses Reveal Key Molecular Determinants of HIV-1 CRF01_AE Resistance to the Entry Inhibitor Temsavir 95%
- Structural and Biochemical Rationale for Enhanced Spike Protein Fitness in Delta and Kappa SARS-CoV-2 Variants 95%
Similar papers in this journal
- Molecular Basis for ADP-ribose Binding to the Macro-X Domain of SARS-CoV-2 Nsp3 94%
- Isoleucine binding and regulation of Escherichia coli and Staphylococcus aureus threonine dehydratase (IlvA) 94%
- An integrative structural model of the full-length gp16 ATPase in bacteriophage phi29 DNA packaging motor 94%
Similar papers in this journal
- Natural variants in SARS-CoV-2 S protein pinpoint structural and functional hotspots; implications for prophylaxis and therapeutics strategies 96%
- In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 94%
- Using machine learning to detect coronaviruses potentially infectious to humans 94%
Similar papers in this journal
- The sensor of the bacterial histidine kinase CpxA is a novel dimer of extracytoplasmic Per-ARNT-Sim (PAS) domains 95%
- Warfarin analogs target disulfide bond-forming enzymes and suggest a residue important for quinone and coumarin binding 95%
- Structural basis of substrate specificity of Helix pomatia AMP deaminase and a chimeric ADGF adenosine deaminase 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.