Evidence map›Paper›PMID 34163622›Full record

ArticleChemical science2021

A microscopic description of SARS-CoV-2 main protease inhibition with Michael acceptors. Strategies for improving inhibitor design.

Carlos A Ramos-Guzmán, J Javier Ruiz-Pernía, Iñaki Tuñón

Open access · diamondAbstract read
In one paragraph

Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
8.0field-weighted citation impact, top 2% of its field
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

27 citing papers in PubMed, 65 citations in OpenAlex.

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  5. CHARMM-GUIJournal of chemical theory and computation · 2025
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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

3 authors at 1 institution in 1 country.

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
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
Universitat de València · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The irreversible inhibition of the main protease of SARS-CoV-2 by a Michael acceptor known as N3 has been investigated using multiscale methods. The noncovalent enzyme-inhibitor complex was simulated using classical molecular dynamics techniques and the pose of the inhibitor in the active site was compared to that of the natural substrate, a peptide containing the Gln-Ser scissile bond. The formation of the covalent enzyme-inhibitor complex was then simulated using hybrid QM/MM free energy methods. After binding, the reaction mechanism was found to be composed of two steps: (i) the activation of the catalytic dyad (Cys145 and His41) to form an ion pair and (ii) a Michael addition where the attack of the Sγ atom of Cys145 to the Cβ atom of the inhibitor precedes the water-mediated proton transfer from His41 to the Cα atom. The microscopic description of protease inhibition by N3 obtained from our simulations is strongly supported by the excellent agreement between the estimated activation free energy and the value derived from kinetic experiments. Comparison with the acylation reaction of a peptide substrate suggests that N3-based inhibitors could be improved by adding chemical modifications that could facilitate the formation of the catalytic dyad ion pair.

Identifiers

PMID34163622
PMCPMC8179461
OpenAlexW3127215132

What OpenQuestion holds

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Registered trials

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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.