Back

Enhanced Target Binding by Leritrelvir Restores Dimerization of Mpro Mutants and Mitigates Drug Resistance

Huang, X.; Kuzmic, P.; Zhang, S.; Guzman, C. A. R.; Chen, X.; Gui, J.; Li, Q.; Yan, S.; Zou, B.; Niu, C.; Zhao, Y.; Lin, H.; Wang, N.; Chen, J.; Chen, X.; Spencer, J.; Mulholland, A. J.; Chen, J.; Zhong, N.; Yang, Z.; Xiong, X.

2026-06-10 molecular biology
10.64898/2026.06.09.730104 bioRxiv
Show abstract

The SARS-CoV-2 main protease (Mpro) has been a major target of antiviral drug development, leading to the development of inhibitors such as nirmatrelvir, the antiviral component of the COVID-19 drug Paxlovid. However, resistance-associated mutations that reduce the efficacy of current Mpro inhibitors, particularly nirmatrelvir, have emerged. Here, we evaluated the inhibitory activity of leritrelvir (RAY1216), an Mpro inhibitor approved in China for COVID-19 monotherapy, against a panel of Mpro variants carrying mutations at 12 resistance-associated residues distributed across four catalytic subsites. Using integrated biochemical, biophysical, structural, and cellular analyses, we demonstrate that leritrelvir retains stronger inhibitory activity against most tested resistant mutants compared with nirmatrelvir. Most of the tested mutations promote Mpro dimer dissociation, with E166V showing a particularly pronounced effect and markedly compromising nirmatrelvir binding. In contrast, thermal shift and size-exclusion chromatography assays demonstrate that leritrelvir binding restores dimerization of these Mpro mutants. Sixteen high-resolution crystal structures reveal that leritrelvir binding re-instates key dimer-interface interactions disrupted by resistance mutations. Mini-replicon assays further confirm leritrelvir to possess enhanced cellular antiviral efficacy compared with nirmatrelvir. Our findings indicate that tighter leritrelvir binding enables more effective inhibition of dissociation-prone Mpro mutants than nirmatrelvir, supporting its use as a more resilient antiviral agent for SARS-CoV-2 treatment.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Antimicrobial Agents and Chemotherapy
187 papers in training set
Top 0.2%
18.3%
2
Nature Communications
5641 papers in training set
Top 17%
10.9%
3
Cell Discovery
57 papers in training set
Top 0.1%
7.2%
4
Antiviral Research
50 papers in training set
Top 0.1%
6.7%
5
Communications Biology
993 papers in training set
Top 3%
4.3%
6
Scientific Reports
3612 papers in training set
Top 35%
3.2%
50% of probability mass above
7
eLife
5828 papers in training set
Top 37%
2.8%
8
Journal of Molecular Biology
232 papers in training set
Top 1%
2.6%
9
International Journal of Molecular Sciences
494 papers in training set
Top 5%
2.6%
10
PLOS Pathogens
820 papers in training set
Top 5%
2.4%
11
PLOS ONE
5266 papers in training set
Top 45%
2.1%
12
Journal of Virology
499 papers in training set
Top 2%
2.1%
13
Cell Chemical Biology
94 papers in training set
Top 0.8%
1.7%
14
EMBO Molecular Medicine
95 papers in training set
Top 0.9%
1.7%
15
Nucleic Acids Research
1281 papers in training set
Top 9%
1.7%
16
Journal of Antimicrobial Chemotherapy
46 papers in training set
Top 0.6%
1.4%
17
Cell Reports
1498 papers in training set
Top 22%
1.3%
18
ACS Central Science
71 papers in training set
Top 0.9%
1.3%
19
mBio
833 papers in training set
Top 9%
1.1%
20
International Journal of Biological Macromolecules
76 papers in training set
Top 1%
1.1%
21
ACS Infectious Diseases
82 papers in training set
Top 1%
1.1%
22
Viruses
332 papers in training set
Top 4%
1.1%
23
npj Antimicrobials and Resistance
11 papers in training set
Top 0.2%
1.1%
24
ChemMedChem
16 papers in training set
Top 0.3%
1.0%
25
Science Advances
1243 papers in training set
Top 31%
0.8%
26
Journal of Biological Chemistry
690 papers in training set
Top 9%
0.8%
27
Science Translational Medicine
127 papers in training set
Top 4%
0.8%
28
Frontiers in Microbiology
427 papers in training set
Top 9%
0.6%
29
Journal of Chemical Information and Modeling
238 papers in training set
Top 3%
0.6%
30
Acta Biochimica et Biophysica Sinica
23 papers in training set
Top 0.7%
0.6%