Evidence map›Paper›PMID 31820222›Full record

ArticleMolecular diversity2021

Molecular modeling of a series of dehydroquinate dehydratase type II inhibitors of Mycobacterium tuberculosis and design of new binders.

Paulo H de S Miranda, Estela M G Lourenço, Alexander M S Morais, Pedro I C de Oliveira, Priscilla S de S N Silverio, Alessandro K Jordão, Euzébio G Barbosa

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 42% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. In Silico Study, Physicochemical, and In Vitro Lipase Inhibitory Activity ofInternational journal of molecular sciences · 2021
    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

7 authors at 1 institution in 1 country.

Paulo H de S MirandaDepartamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0002-7893-8408
Estela M G LourençoDepartamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0003-2708-4526
Alexander M S MoraisDepartamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0002-8461-0716
Pedro I C de OliveiraPrograma de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0001-7801-171X
Priscilla S de S N SilverioPrograma de Pós-Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0003-1687-6907
Alessandro K JordãoDepartamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil.ORCID http://orcid.org/0000-0002-2920-3966
Euzébio G BarbosaDepartamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil. euzebiogb@gmail.com.ORCID http://orcid.org/0000-0002-7685-9618
Universidade Federal do Rio Grande do Norte · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is still responsible for a large number of fatal cases, especially in developing countries with alarming rates of incidence and prevalence worldwide. Mycobacterium tuberculosis has a remarkable ability to develop new resistance mechanisms to the conventional antimicrobials treatment. Because of this, there is an urgent need for novel bioactive compounds for its treatment. The dehydroquinate dehydratase II (DHQase II) is considered a key enzyme of shikimate pathway, and it can be used as a promising target for the design of new bioactive compounds with antibacterial action. The aim of this work was the construction of QSAR models to aid the design of new potential DHQase II inhibitors. For that purpose, various molecular modeling approaches, such as activity cliff, QSAR models and computer-aided ligand design were utilized. A predictive in silico 4D-QSAR model was built using a database comprising 86 inhibitors of DHQase II, and the model was used to predict the activity of the designed ligands. The obtained model proved to predict well the DHQase II inhibition for an external validation dataset ([Formula: see text] = 0.72). Also, the Activity Cliff analysis shed light on important structural features applied to the ligand design.

Indexed as

Antitubercular AgentsBinding SitesDrug DesignEnzyme InhibitorsHydro-LyasesLigandsModels, MolecularMycobacterium tuberculosisQuantitative Structure-Activity Relationship3-dehydroquinate dehydrataseAntitubercular AgentsEnzyme InhibitorsHydro-LyasesLigandsActivity cliffDHQase IIDrug designQSARShikimateTuberculosis

Identifiers

PMID31820222
OpenAlexW2995733487

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.