In Silico-Based Identification of Natural Inhibitors from Traditionally Used Medicinal Plants That Can Inhibit Dengue Infection

Overview

Journal Mol Biotechnol

Publisher Springer

Specialties Biotechnology
Molecular Biology

Date 2024 Jun 4

PMID 38834897

Authors

Md Tarikul Islam

Md Aktaruzzaman

Ahmed Saif

Ayesha Akter

Mashooq Ahmad Bhat

Mirza Mahfuj Hossain

S M Nur Alam

Rifat Rayhan

Saira Rehman

Muhammad Yaseen

Md Obayed Raihan

Affiliations

Soon will be listed here.

Abstract

Dengue fever (DF) is an endemic disease that has become a public health concern around the globe. The NS3 protease-helicase enzyme is an important target for the development of antiviral drugs against DENV (dengue virus) due to its impact on viral replication. Inhibition of the activity of the NS3 protease-helicase enzyme complex significantly inhibits the infection associated with DENV. Unfortunately, there are no scientifically approved antiviral drugs for its prevention. However, this study has been developed to find natural bioactive molecules that can block the activity of the NS3 protease-helicase enzyme complex associated with DENV infection through molecular docking, MM-GBSA (molecular mechanics-generalized born surface area), and molecular dynamics (MD) simulations. Three hundred forty-two (342) compounds selected from twenty traditional medicinal plants were retrieved and screened against the NS3 protease-helicase protein by molecular docking and MM-GBSA studies, where the top six phytochemicals have been identified based on binding affinities. The six compounds were then subjected to pharmacokinetics and toxicity analysis, and we conducted molecular dynamics simulations on three protein-ligand complexes to validate their stability. Through computational analysis, this study revealed the potential of the two selected natural bioactive inhibitors (CID-440015 and CID-7424) as novel anti-dengue agents.

Citing Articles

Comprehensive metabolite profiling and evaluation of anti-nociceptive and anti-inflammatory potencies of (Wurmb.) leaves: Experimental and in-silico approaches.

Islam F, Aktaruzzaman M, Islam M, Rodru F, Yesmine S Heliyon. 2025; 11(3):e42074.

PMID: 39975840 PMC: 11835622. DOI: 10.1016/j.heliyon.2025.e42074.

Identification of acetylcholinesterase inhibitors from traditional medicinal plants for Alzheimer's disease using and machine learning approaches.

Islam M, Aktaruzzaman M, Saif A, Hasan A, Sourov M, Sikdar B RSC Adv. 2024; 14(47):34620-34636.

PMID: 39483377 PMC: 11526779. DOI: 10.1039/d4ra05073h.

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