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Updated: Jun 14, 2025
High-throughput Antiviral Assays to Screen for Inhibitors of Zika Virus Replication
Published on: October 30, 2021
Employing Machine Learning-Based QSAR for Targeting Zika Virus NS3 Protease: Molecular Insights and Inhibitor
Hisham N Altayb1, Hanan Ali Alatawi2
1Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Abstract:
Zika virus infection is a mosquito-borne viral disease that has become a global health concern recently. Zika virus belongs to the Flavivirus genus and is primarily transmitted by Aedes mosquitoes. Prevention of Zika virus infection involves avoiding mosquito bites by using repellent, wearing protective clothing, and staying in screened areas, especially for pregnant women. Treatment focuses on managing symptoms with rest, fluids, and acetaminophen, with close monitoring for pregnant women. Currently, there is no specific antiviral treatment or vaccine for the Zika virus, highlighting the importance of prevention strategies to control its spread. Therefore, in this study, the Zika virus non-structural protein NS3 was targeted to inhibit Zika infection by identifying the novel inhibitor through an in silico approach. Here, 2864 natural compounds were screened using a machine learning-based QSAR model, and later docking was performed to select the potential target. Subsequently, Tanimoto similarity and clustering were performed to obtain the potential target. The three most potential compounds were obtained: (a) 5297, (b) 432449, and (c) 85137543. The protein-ligand complex's stability and flexibility were then investigated by dynamic modelling. The 300 ns simulation showed that 5297 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 5297 demonstrated a superior binding free energy (ΔG = -20.81 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 5297 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the NS2B-NS3 protease target implicated in Zika virus infection.
Insights
Researchers identified a potential new drug, compound 5297, to inhibit Zika virus infection. This novel inhibitor targets the Zika virus NS2B-NS3 protease, offering hope for future antiviral treatments.
Area of Science:
- Virology
- Infectious Diseases
- Drug Discovery
Background:
- Zika virus is a mosquito-borne illness posing a global health threat.
- Current prevention relies on avoiding mosquito bites, and treatment is supportive.
- No specific antiviral treatments or vaccines are available, emphasizing the need for new therapeutic strategies.
Purpose of the Study:
- To identify novel inhibitors of Zika virus infection using an in silico approach.
- To target the Zika virus non-structural protein NS3, crucial for viral replication.
Main Methods:
- Screened 2864 natural compounds using a machine learning-based Quantitative Structure-Activity Relationship (QSAR) model.
- Performed molecular docking, Tanimoto similarity, and clustering to identify potential inhibitors.
- Utilized molecular dynamics simulations and MM/GBSA calculations to assess binding affinity and stability.
Main Results:
- Identified three potential compounds: 5297, 432449, and 85137543.
- Compound 5297 demonstrated the most stable interaction with the NS2B-NS3 protease, with consistent hydrogen bond formation.
- Compound 5297 exhibited superior binding free energy (ΔG = -20.81 kcal/mol) and significant therapeutic potential.
Conclusions:
- Compound 5297 shows promise as a potential therapeutic agent against Zika virus infection.
- Further experimental validation is warranted to confirm its efficacy as an inhibitor of the NS2B-NS3 protease.
