In Silico Identification of JMJD3 Demethylase Inhibitors.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Structural Biology

Background:

• Demethylase enzymes play critical roles in cellular processes, and their dysregulation is implicated in various diseases.
• Developing potent and selective demethylase inhibitors is a key goal in drug discovery.
• Existing screening methods often require significant experimental resources.

Purpose of the Study:

• To establish a novel multistep in silico screening protocol for identifying demethylase inhibitors.
• To apply this protocol to discover inhibitors of JMJD3 (Jumonji domain-containing protein 3).
• To validate the computational predictions through experimental methods, including structural analysis.

Main Methods:

• A multistep computational screening strategy combining high-throughput docking, 3D-pharmacophore searching, classical force field minimization, and semiempirical quantum mechanical calculations.
• Incorporation of solvation effects using the finite-difference Poisson equation for enhanced accuracy.
• Application to JMJD3 demethylase and subsequent experimental validation.

Main Results:

• Identification of a dozen low-micromolar inhibitors for JMJD3, spanning five distinct chemical classes.
• Successful crystallization and structure determination of a JMJD3 inhibitor (compound 8) in complex with UTX (an enzyme in the same subfamily).
• Validation of the predicted binding mode through the crystal structure, confirming the efficacy of the computational approach.

Conclusions:

• The developed multistep in silico protocol is effective for discovering novel demethylase inhibitors.
• Compound 8 represents a promising lead compound with a favorable ligand efficiency, suitable for further optimization.
• This computational strategy offers an efficient and accurate method for identifying potential drug candidates targeting demethylases.