Correlating Predicted Reactivities with Experimental Inhibition Data of Covalent ChlaDUB1 Inhibitors

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Enzyme Inhibition

Background:

• Covalent inhibitors offer prolonged enzyme inactivation compared to noncovalent ones.
• Computational methods predict reactivity of covalent inhibitors, but their enzyme-specific relevance is understudied.
• Current validation often relies on simple nucleophile assays, not the enzyme active site.

Purpose of the Study:

• To assess the transferability of computational reactivity predictions to actual enzyme inhibition.
• To investigate the role of the enzyme environment in covalent inhibitor efficacy.
• To guide drug discovery efforts for covalent inhibitors targeting ChlaDUB1.

Main Methods:

• Employed a Density Functional Theory (DFT)-based computational approach.
• Designed and synthesized a series of covalent inhibitors targeting ChlaDUB1.
• Evaluated inhibitor reactivity within the specific enzyme context.

Main Results:

• Demonstrated that computational reactivity predictions need enzyme-specific validation.
• Highlighted the significant influence of geometrical congruence between ligands and the enzyme.
• Showed that in silico reactivity does not always translate directly to in situ efficacy.

Conclusions:

• Geometric factors are critical when assessing covalent inhibitor reactivity in drug discovery.
• Computational predictions must account for the enzyme's unique environment for accurate efficacy assessment.
• This study provides a framework for refining covalent drug design targeting enzymes like ChlaDUB1.