Fast and Effective Prediction of the Absolute Binding Free Energies of Covalent Inhibitors of SARS-CoV-2 Main Protease and 20S Proteasome

The COVID-19 pandemic has been a public health emergency with continuously evolving deadly variants around theglobe. Among many preventive and therapeutic strategies, the design of covalent inhibitors targeting the main protease (Mpro)ofSARS-CoV-2 that causes COVID-19 has been one of the hotly pursued areas. Currently, about 30% of marketed drugs that targetenzymes are covalent inhibitors. Such inhibitors have been shown in recent years to have many advantages that counteract pastreservation of their potential off-target activities, which can be minimized by modulation of the electrophilic warhead andsimultaneous optimization of nearby noncovalent interactions. This process can be greatly accelerated by exploration of bindingaffinities using computational models, which are not well-established yet due to the requirement of capturing the chemical nature ofcovalent bond formation. Here, we present a robust computational method for effective prediction of absolute binding free energies(ABFEs) of covalent inhibitors. This is done by integrating the protein dipoles Langevin dipoles method (in the PDLD/S-LRA/beta version) with quantum mechanical calculations of the energetics of the reaction of the warhead and its amino acid target, in water.This approach evaluates the combined effects of the covalent and noncovalent contributions. The applicability of the method isillustrated by predicting the ABFEs of covalent inhibitors of SARS-CoV-2 Mproand the 20S proteasome. Our results are found to bereliable in predicting ABFEs for cases where the warheads are significantly different. This computational protocol might be apowerful tool for designing effective covalent inhibitors especially for SARS-CoV-2 Mproand for targeted protein degradation.