Exploring structural diversity and dynamic stability of small-molecule PRMT5 inhibitors through machine...

Area of Science:

• Epigenetics
• Computational Chemistry
• Drug Discovery

Background:

• Protein arginine methyltransferase 5 (PRMT5) is a key epigenetic enzyme.
• PRMT5 overexpression is linked to MTAP-deleted cancers like glioblastoma and pancreatic adenocarcinoma.
• PRMT5 plays a role in chromatin organization, RNA splicing, and oncogenic signaling.

Purpose of the Study:

• To identify novel inhibitors of PRMT5 using an integrative computational approach.
• To evaluate the binding affinity and stability of potential PRMT5 inhibitors.
• To explore the therapeutic relevance of PRMT5 inhibition in epigenetic cancer therapy.

Main Methods:

• Quantitative structure-activity relationship (QSAR) modeling with machine learning (Random Forest).
• Molecular docking and molecular dynamics (MD) simulations.
• Network pharmacology analysis.

Main Results:

• QSAR models identified CHEMBL4539612 and CHEMBL4577464 as potent PRMT5 inhibitors.
• Both candidates exhibited high binding affinity and stable interactions within the PRMT5 active site.
• Network pharmacology confirmed PRMT5's role in histone methylation and spliceosomal assembly in cancers.

Conclusions:

• CHEMBL4539612 and CHEMBL4577464 are promising scaffolds for selective PRMT5 inhibitor development.
• These compounds could advance epigenetic cancer therapy for MTAP-deleted malignancies.
• The study highlights the utility of integrated computational methods in drug discovery.