Discovery of Novel Chemotype LRRK2 Inhibitors Through AlphaFold2-Generated Structure-Based Docking Screen.

Area of Science:

• Structural biology
• Computational chemistry
• Drug discovery

Background:

• AlphaFold2-predicted structures offer potential for structure-based drug design.
• Experimental validation of using apo-form structures in docking screens is limited.
• Leucine-rich repeat kinase 2 (LRRK2) is a key target in several diseases.

Purpose of the Study:

• To report novel chemotype inhibitors targeting the LRRK2 kinase domain.
• To demonstrate the utility of AlphaFold2-derived structures in virtual screening.
• To explore the discovery of new kinase inhibitors using computational methods.

Main Methods:

• Generated an ensemble of LRRK2 structures using MODELLER from an AlphaFold2 template.
• Performed a docking screen of ~1.3 million small molecules against the selected LRRK2 conformer.
• Conducted enzyme-based assays to validate identified inhibitors and assess selectivity.

Main Results:

• Discovered ten novel small molecules with inhibitory activity against LRRK2 (Ki < 15 μM).
• Identified one inhibitor with sub-micromolar potency.
• Initial selectivity profiling suggested that novel chemical structures alone may not guarantee selectivity.

Conclusions:

• The computational approach using AlphaFold2-predicted structures is effective for identifying novel kinase inhibitors.
• This strategy is broadly applicable for drug discovery targeting proteins lacking experimental structures.
• Further optimization is needed to ensure selectivity of identified inhibitors.