Abstract
Protein arginine methyltransferase 5 (PRMT5), which symmetrically dimethylates cytosolic and nuclear proteins, has been demonstrated as an important cancer therapeutic target. In recent years, many advanced achievements in PRMT5 inhibitor development have been made. Most PRMT5 inhibitors in the clinical trial focus on targeting the C-terminal catalytic domain, whereas developing small molecules to interrupt the PRMT5/pICLn (methylosome subunit) protein-protein interface is also of great importance for inhibiting PRMT5. Here, we describe a machine-learning-based virtual screening method and use this novel pipeline to screen small-molecule inhibitors of the PRMT5/pICLn interaction. 18 compounds were manually selected for experimental testing. One compound, Z319334062, showed surface plasmon resonance-binding affinity to the target (K D = 21.5 μM) and dose-dependently inhibited symmetric dimethylation levels in patient-derived glioblastoma cell lines.