Affinity of drug candidates binding to SARS-CoV-2 PLpro assessed using a generalized-ensemble method

Area of Science:

• Computational chemistry
• Molecular dynamics
• Drug discovery

Background:

• Ligand-receptor binding is crucial for biological processes and drug development.
• Accurate screening methods are needed to identify effective inhibitors.

Purpose of the Study:

• To establish and validate a novel computational procedure for screening compounds that inhibit ligand-receptor binding.
• To assess the efficacy of this method in identifying inhibitors for SARS-CoV-2 papain-like protease (PLpro).

Main Methods:

• Employed multidimensional virtual-system coupled molecular dynamics (mD-VcMD), a generalized ensemble method.
• Simulated flexible receptors and compounds in explicit solvent to generate free-energy landscapes.
• Analyzed free-energy basins and binding modes for four SARS-CoV-2 PLpro inhibitors.

Main Results:

• The mD-VcMD method generated funnel-like free-energy landscapes for all tested compounds.
• Probabilities in free-energy basins correlated well with measured dissociation constants.
• Structural clustering revealed binding modes that correlated with enzyme inhibitory activity.

Conclusions:

• The proposed mD-VcMD procedure is effective for screening compounds and selecting potential inhibitors.
• This method provides a reliable approach for identifying drug candidates targeting viral proteases like SARS-CoV-2 PLpro.