IKKbeta inhibitors identification part I: homology model assisted structure based virtual screening

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Pharmacology

Background:

• Inhibition of IKKbeta is a promising therapeutic strategy for inflammatory and autoimmune diseases.
• Targeting NF-kappaB signaling pathway offers potential treatment avenues.

Purpose of the Study:

• To identify novel small molecules that inhibit IKKbeta using structure-based virtual screening.
• To discover lead-like compounds with potential therapeutic applications against IKKbeta.

Main Methods:

• Development of homology models for IKKbeta based on known kinase crystal structures.
• Structure-based virtual screening of the ChemDiv database to identify potential inhibitors.
• In vitro enzyme inhibition assays to evaluate the efficacy of selected compounds.

Main Results:

• Virtual screening identified 277 potential hits, with 75 compounds selected for further testing.
• Six novel compounds demonstrated inhibitory activity against IKKbeta.
• One compound exhibited the highest inhibition rate (82.09% at 10 μM) and an IC50 of 1.76 μM.

Conclusions:

• Structure-based virtual screening combined with biological evaluation is effective for discovering novel IKKbeta inhibitors.
• Identified compounds represent promising leads for the development of new treatments for inflammatory and autoimmune conditions.
• The amino acid residue Glu172 was implicated in inhibitor selectivity based on docking analyses.