Evidence map›Paper›PMID 35843284›Full record

ArticleJournal of molecular biology2022

Allosteric Hotspots in the Main Protease of SARS-CoV-2.

Léonie Strömich, Nan Wu, Mauricio Barahona, Sophia N Yaliraki

Abstract read
In one paragraph

Article in Journal of molecular biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. A potential allosteric inhibitor of SARS-CoV-2 main protease (MFrontiers in molecular biosciences · 2024
    Article
  6. Mapping allosteric pathway in NIa-Pro using computational approach.Quantitative biology (Beijing, China) · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
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

4 authors.

Léonie StrömichDepartment of Chemistry Imperial College London, United Kingdom.
Nan WuDepartment of Chemistry Imperial College London, United Kingdom.
Mauricio BarahonaDepartment of Mathematics Imperial College London, United Kingdom.
Sophia N YalirakiDepartment of Chemistry Imperial College London, United Kingdom. Electronic address: s.yaliraki@imperial.ac.uk.

Funding

Wellcome Trust 215360/Z/19/Z
6 · The paper itself

Abstract

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

Indexed as

Coronavirus 3C ProteasesAllosteric SiteMolecular Docking SimulationProtein Conformation3C-like proteinase, SARS-CoV-2Coronavirus 3C Proteasesallosteric site predictionatomistic graph representationgraph theorySARS-CoV-2

Identifiers

PMID35843284
PMCPMC9288249

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.