Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Chemistry

Background:

• SARS-CoV-2 papain-like protease (PLpro) is a critical therapeutic target.
• Existing inhibitors like GRL0617 can be improved for enhanced binding.
• Understanding binding cooperativity is key to optimizing drug design.

Purpose of the Study:

• Investigate the origin of binding cooperativity between PLpro and optimized GRL0617.
• Quantify the contributions of enthalpy and entropy to cooperativity.
• Identify specific residues involved in the cooperative binding mechanism.

Main Methods:

• Molecular dynamics (MD) simulations.
• Interaction entropy (IE) method.
• Binding free energy calculations using thermodynamic integration.
• Molecular mechanics/generalized Born surface area (MM/GBSA) for enthalpy-entropy decomposition.

Main Results:

• Optimized GRL0617 regions demonstrate cooperativity with PLpro, increasing binding affinity.
• Thermodynamic integration confirmed cooperativity in the improved drug-PLpro complex.
• Entropic changes were identified as a significant contributor to the observed cooperativity.
• Specific residues (P248, Q269, T301) were found to be crucial for cooperativity, with optimization minimizing entropic loss.

Conclusions:

• Drug optimization leveraging cooperativity enhances binding affinity to SARS-CoV-2 PLpro.
• Entropic contributions, stabilized by inhibitor optimization at key residues, are central to cooperativity.
• This research provides a theoretical framework for developing drugs that utilize multi-locus binding to reduce entropic penalties.