Bayesian Inference Elucidates the Catalytic Competency of the SARS-CoV-2 Main Protease 3CL^pro

The main protease of SARS-CoV-2, 3CL^pro, 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 3CL^pro and its highly similar SARS-CoV homologue conflict dramatically. Values of the dimeric K_d and enzymic k_cat/K_M 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 3CL^pro forms a dimer at pH 7.5 that has K_d = 16 ± 4 nM and is capable of catalysis with k_cat = 9.9 ± 1.5 s^-1, K_M = 0.23 ± 0.01 mM, and k_cat/K_M = (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 3CL^pro is a more capable catalyst than appreciated previously, which has important implications for the design of antiviral therapeutic agents that target 3CL^pro.