npj VirusesMolecular BiologyPeer-reviewed journal article
Identification of key residues in MERS-CoV and SARS-CoV-2 main proteases for resistance against clinically applied inhibitors nirmatrelvir and ensitrelvir
Laura KrismeriD, Helge SchöppeiD, Stefanie RauchiD, David BanteiD, Bernhard SprengeriD, Andreas NaschbergeriD, Francesco CostacurtaiD, Anna Fürst, Anna SauerweiniD, Bernhard RuppiD, Teresa KasereriD, Dorotheé von LaeriD, Emmanuel HeilmanniD
Abstract
The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an epidemic, zoonotically emerging pathogen initially reported in Saudi Arabia in 2012. MERS-CoV has the potential to mutate or recombine with other coronaviruses, thus acquiring the ability to efficiently spread among humans and become pandemic. Its high mortality rate of up to 35 % and the absence of effective targeted therapies call for the development of antiviral drugs for this pathogen. Since the beginning of the SARS-CoV-2 pandemic, extensive research has focused on identifying protease inhibitors for the treatment of SARS-CoV-2. Our intention was therefore to assess whether these protease inhibitors are viable options for combating MERS-CoV. To that end, we used previously established protease assays to quantify inhibition of the SARS-CoV-2 and MERS-CoV main proteases. Furthermore, we selected MERS-CoV-Mpro mutants resistant against nirmatrelvir, the most effective inhibitor of this protease, with a safe, surrogate virus-based system, and suggest putative resistance mechanisms. Notably, nirmatrelvir demonstrated effectiveness against various viral proteases, illustrating its potential as a broad-spectrum coronavirus inhibitor. To adress the inherent resistance of MERS-CoV-Mpro to ensitrelvir, we applied directed mutagenesis to a key ensitrelvir-interacting residue and provided structural models. One-Sentence SummaryWe investigate antivirals for MERS-CoV with a pool of SARS-CoV-2 antiviral drugs and study potential resistances developing against those drugs.
- Citations
- 22
- FWCI
- 7.61
- Field percentile
- top 2%
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