Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations.

Area of Science:

• Computational chemistry
• Molecular dynamics
• Drug discovery

Background:

• SARS-CoV-2 Mpro is a key target for antiviral drug development.
• Understanding inhibitor binding and unbinding is crucial for designing effective therapeutics.

Purpose of the Study:

• To evaluate the efficiency and accuracy of umbrella sampling (US) simulations for studying small molecule inhibitors of SARS-CoV-2 Mpro.
• To elucidate the unbinding pathways and identify key residues involved in ligand dissociation.

Main Methods:

• Umbrella sampling (US) simulations were employed to analyze ligand interactions with SARS-CoV-2 Mpro.
• The accuracy of US was compared with linear interaction energy (LIE) and fast pulling of ligand (FPL) methods using correlation coefficients and root mean square error.
• Receiver operating characteristic-area under the curve analysis was used to assess the classification performance of US.

Main Results:

• US simulations demonstrated accuracy comparable to LIE and FPL methods.
• US showed a slightly smaller correlation coefficient than LIE but similar to FPL.
• The root mean square error for US was lower than LIE.
• US simulations provided detailed insights into ligand unbinding pathways, highlighting the role of residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192, and Ala193.

Conclusions:

• Umbrella sampling is an efficient and accurate method for determining SARS-CoV-2 Mpro inhibitor binding affinity and unbinding pathways.
• The identified key residues offer potential sites for modification to alter inhibitor binding mechanisms and enhance drug efficacy.