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Computational design, docking, and molecular dynamics simulation study of RNA helicase inhibitors of dengue virus.

Overview of attention for article published in Journal of Vector Borne Diseases, May 2024
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Title
Computational design, docking, and molecular dynamics simulation study of RNA helicase inhibitors of dengue virus.
Published in
Journal of Vector Borne Diseases, May 2024
DOI 10.4103/jvbd.jvbd_188_23
Pubmed ID
Authors

Raghunath Satpathy, Sonali Acharya, Rashmiranjan Behera

Abstract

RNA viruses are complex pathogens in terms of their genetic makeup, mutation frequency, and transmission modes. They contain the RNA helicase enzyme, which plays a crucial role in the viral genome replication process. This work aims to develop and screen a potential molecule that could function as a dengue virus (DENV) RNA helicase inhibitor. The present study was performed by taking 26 potential derivatives of gedunin phytochemicals from the PubChem database as ligands. The binding study of the compounds were analyzed by in silico docking method considering DENV RNA helicase enzyme as the receptor. After a thorough analysis of the docking scores, toxicity, and physicochemical properties, compound tetrahydrogedunin was obtained as the best. Based on tetrahydrogedunin molecular structure, 100 drug-like molecules were designed using the Data Warrior tool. After the screening process for drug-likeness and ADMET properties, the derivative number 42 was considered as the promising. Further comparative docking of derivative 42 and a standard inhibitor molecule ST-610 with DENV RNA helicase enzyme showed binding affinity as -10.0 kcal/mol and -9.6 kcal/mol, respectively. The favorable interaction between DENV RNA helicase and derivative 42 was further validated by 50 nanoseconds molecular dynamics simulation and MM-GBSA analysis. Since the antiviral activity of derivative 42 has not been reported till date, the compound was predicted as a novel therapeutic molecule that can act against the dengue virus (DENV) RNA helicase enzyme.

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 May 2024.
All research outputs
#17,643,761
of 25,861,751 outputs
Outputs from Journal of Vector Borne Diseases
#190
of 430 outputs
Outputs of similar age
#75,448
of 148,780 outputs
Outputs of similar age from Journal of Vector Borne Diseases
#1
of 6 outputs
Altmetric has tracked 25,861,751 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 430 research outputs from this source. They receive a mean Attention Score of 1.8. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 148,780 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them