Evidence map›Paper›PMID 28155702›Full record

ArticleBMC bioinformatics2016

Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs.

Dhwani Dholakia, Sukriti Goyal, Salma Jamal, Aditi Singh, Asmita Das, Abhinav Grover

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Dhwani DholakiaDepartment of Biotechnology, Delhi Technological University, New Delhi, 110042, India.
Sukriti GoyalDepartment of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.
Salma JamalDepartment of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India.
Aditi SinghDepartment of Biotechnology, TERI University, New Delhi, 110070, India.
Asmita DasDepartment of Biotechnology, Delhi Technological University, New Delhi, 110042, India.
Abhinav GroverSchool of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India. abhinavgr@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfluenza virus spreads infection by two main surface glycoproteins, namely hemagglutinin (HA) and neuraminidase (NA). NA cleaves the sialic acid receptors eventually releasing newly formed virus particles which then invade new cells. Inhibition of NA could limit the replication of virus to one round which is insufficient to cause the disease.

resultsAn experimentally reported series of acylguanidine zanamivir derivatives was used to develop GQSAR model targeting NA in different strains of influenza virus, H1N1 and H3N2. A combinatorial library was developed and their inhibitory activities were predicted using the GQSAR model.

conclusionThe top leads were analyzed by docking which revealed the binding modes of these inhibitors in the active site of NA (150-loop). The top compound (AMA) was selected for carrying out molecular dynamics simulations for 15 ns which provided insights into the time dependent dynamics of the designed leads. AMA possessed a docking score of -8.26 Kcal/mol with H1N1 strain and -7.00 Kcal/mol with H3N2 strain. Ligand-bound complexes of both H1N1 and H3N2 were observed to be stable for 11 ns and 7 ns respectively. ADME descriptors were also calculated to study the pharmacokinetic properties of AMA which revealed its drug-like properties.

Indexed as

Drug DesignAntiviral AgentsCatalytic DomainEnzyme InhibitorsHumansInfluenza A Virus, H1N1 SubtypeInfluenza A Virus, H3N2 SubtypeInfluenza, HumanMolecular Dynamics SimulationNeuraminidaseZanamivirAntiviral AgentsEnzyme InhibitorsNeuraminidaseZanamivirH1N1H3N2InfluenzaNANeuraminidaseQSAR

Identifiers

PMID28155702
PMCPMC5259988

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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.