Evidence map›Paper›PMID 36908962›Full record

ArticleChemical science2023

The impact of SARS-CoV-2 3CL protease mutations on nirmatrelvir inhibitory efficiency. Computational insights into potential resistance mechanisms.

Carlos A Ramos-Guzmán, Milorad Andjelkovic, Kirill Zinovjev, J Javier Ruiz-Pernía, Iñaki Tuñón

Abstract read
In one paragraph

Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Carlos A Ramos-GuzmánDepartamento de Química Física, Universidad de Valencia 46100 Burjassot Spain ignacio.tunon@uv.es j.javier.ruiz@uv.es.ORCID https://orcid.org/0000-0002-7701-377X
Milorad AndjelkovicDepartamento de Química Física, Universidad de Valencia 46100 Burjassot Spain ignacio.tunon@uv.es j.javier.ruiz@uv.es.
Kirill ZinovjevDepartamento de Química Física, Universidad de Valencia 46100 Burjassot Spain ignacio.tunon@uv.es j.javier.ruiz@uv.es.
J Javier Ruiz-PerníaDepartamento de Química Física, Universidad de Valencia 46100 Burjassot Spain ignacio.tunon@uv.es j.javier.ruiz@uv.es.ORCID https://orcid.org/0000-0002-4640-0419
Iñaki TuñónDepartamento de Química Física, Universidad de Valencia 46100 Burjassot Spain ignacio.tunon@uv.es j.javier.ruiz@uv.es.ORCID https://orcid.org/0000-0002-6995-1838

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of antiviral drugs can promote the appearance of mutations in the target protein that increase the resistance of the virus to the treatment. This is also the case of nirmatrelvir, a covalent inhibitor of the 3CL protease, or main protease, of SARS-CoV-2. In this work we show how the by-residue decomposition of noncovalent interactions established between the drug and the enzyme, in combination with an analysis of naturally occurring mutations, can be used to detect potential mutations in the 3CL protease conferring resistance to nirmatrelvir. We also investigate the consequences of these mutations on the reaction mechanism to form the covalent enzyme-inhibitor complex using QM/MM methods. In particular, we show that the E166V variant of the protease displays smaller binding affinity to nirmatrelvir and larger activation free energy for the formation of the covalent complex, both factors contributing to the observed resistance to the treatment with this drug. The conclusions derived from our work can be used to anticipate the consequences of the introduction of nirmatrelvir in the fitness landscape of the virus and to design new inhibitors adapted to some of the possible resistance mechanisms.

Identifiers

PMID36908962
PMCPMC9993853

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.