Bayesian Inference Elucidates the Catalytic Competency of the SARS-CoV-2 Main Protease 3CLpro.

Abstract

The main protease of SARS-CoV-2, 3CLpro, is a dimeric enzyme that is indispensable to viral replication and presents an attractive opportunity for therapeutic intervention. Previous reports regarding the key properties of 3CLpro and its highly similar SARS-CoV homologue conflict dramatically. Values of the dimeric Kd and enzymic kcat/KM differ by 106- and 103-fold, respectively. Establishing a confident benchmark of the intrinsic capabilities of this enzyme is essential for combating the current pandemic as well as potential future outbreaks. Here, we use enzymatic methods to characterize the dimerization and catalytic efficiency of the authentic protease from SARS-CoV-2. Specifically, we use the rigor of Bayesian inference in a Markov Chain Monte Carlo analysis of progress curves to circumvent the limitations of traditional Michaelis-Menten initial rate analysis. We report that SARS-CoV-2 3CLpro forms a dimer at pH 7.5 that has Kd = 16 ± 4 nM and is capable of catalysis with kcat = 9.9 ± 1.5 s-1, KM = 0.23 ± 0.01 mM, and kcat/KM = (4.3 ± 0.7) × 104 M-1 s-1. We also find that enzymatic activity decreases substantially in solutions of high ionic strength, largely as a consequence of impaired dimerization. We conclude that 3CLpro is a more capable catalyst than appreciated previously, which has important implications for the design of antiviral therapeutic agents that target 3CLpro.