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Quantitative Structure-activity Relationship-based Neural Network Model in Screening Potential Inhibitors from Brown
Abdullah Alqarni1, Jagadish Hosmani1, Saeed Alassiri1
1Department of Diagnostic Dental Sciences & Oral Biology, College of Dentistry, King Khalid University, Abha, Saudi Arabia.
Introduction And Aim:
Oral squamous cell carcinomas (OSCCs) are one of the most frequently diagnosed head and neck cancers with a poor prognosis despite the advancements in diagnostic techniques and treatment strategies. The progression of OSCC is driven by several molecular mechanisms, among them the overexpression of transcription factor RelA, which plays a crucial role by correlating with the clinicopathological characteristics.
Methods:
This systematic investigation focused on identifying the top 25 crucial molecular descriptors to predict the RelA inhibitor through the quantitative structure-activity relationship (QSAR)-based artificial neural network model.
Results:
In this study, the developed multilayer perceptron model showed an accuracy of 91.37% in the classification of active inhibitors, with a Matthews correlation coefficient (MCC) of 0.89. Then the model was assessed for the 1221 brown algae-derived compounds, identifying 1014 as the most active RelA inhibitors. Further, molecular docking revealed that phlorethopentafuhalol-A had a higher affinity based on the binding energy of -8.45 kcal/mol than the known RelA inhibitors (-5.30 to -1.31 kcal/mol). Molecular dynamics (MD) simulation confirmed that phlorethopentafuhalol-A formed a stable conformation with the RelA based on the trajectory analysis.
Conclusions:
Overall, this analysis demonstrated that phlorethopentafuhalol-A could be a potential RelA inhibitor that may be useful in the treatment of OSCC on further investigation.
Clinical Relevance:
The multilayer perceptron model extracted relevant descriptors to predict the inhibitory properties of each compound. Using these descriptors, potential inhibitory molecules were predicted from a dataset of compounds sourced from brown algae. The predicted molecule was then evaluated for its interaction with the RelA protein through molecular docking and MD simulations.
Insights
Researchers identified phlorethopentafuhalol-A from brown algae as a promising inhibitor of RelA, a key factor in oral squamous cell carcinoma (OSCC) progression. This discovery offers a potential new avenue for OSCC treatment.
Area of Science:
- Computational chemistry
- Drug discovery
- Oncology
Background:
- Oral squamous cell carcinoma (OSCC) is a prevalent head and neck cancer with a poor prognosis.
- Overexpression of the transcription factor RelA is a key driver in OSCC progression.
- Targeting RelA presents a potential therapeutic strategy for OSCC.
Purpose of the Study:
- To identify novel RelA inhibitors for OSCC treatment.
- To develop a predictive quantitative structure-activity relationship (QSAR) model for RelA inhibitors.
- To evaluate natural compounds from brown algae as potential RelA inhibitors.
Main Methods:
- Development of a QSAR-based artificial neural network (multilayer perceptron) model.
- Screening of 1221 brown algae-derived compounds for RelA inhibitory activity.
- Molecular docking and molecular dynamics (MD) simulations to assess binding affinity and stability.
Main Results:
- The QSAR model achieved 91.37% accuracy in classifying active RelA inhibitors (MCC=0.89).
- 1014 out of 1221 screened compounds were identified as potent RelA inhibitors.
- Phlorethopentafuhalol-A exhibited superior binding affinity (-8.45 kcal/mol) compared to known inhibitors and formed stable interactions with RelA via MD simulation.
Conclusions:
- Phlorethopentafuhalol-A is a potential therapeutic candidate for OSCC treatment.
- The developed QSAR model effectively predicts RelA inhibitory properties.
- Natural products from brown algae represent a promising source for novel anti-cancer drug discovery.
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