Serine protease inhibitors of the Kunitz-bovine pancreatic trypsin inhibitor (BPTI) family are ubiquitous biological regulators of proteolysis. These small proteins are resistant to proteolysis, but can be slowly cleaved within the protease-binding loop by target proteases, thereby compromising their activity. For the human protease mesotrypsin, this cleavage is especially rapid. Here, we aimed to stabilize the Kunitz domain structure against proteolysis through disulfide engineering. Substitution within the Kunitz inhibitor domain of the amyloid precursor protein (APPI) that incorporated a new disulfide bond between residues 17 and 34 reduced proteolysis by mesotrypsin 74-fold. Similar disulfide engineering of tissue factor pathway inhibitor-1 Kunitz domain 1 (^KD1TFPI1) and bikunin Kunitz domain 2 (^KD2bikunin) likewise stabilized these inhibitors against mesotrypsin proteolysis 17- and 6.6-fold, respectively. Crystal structures of disulfide-engineered APPI and ^KD1TFPI1 variants in a complex with mesotrypsin at 1.5 and 2.0 Å resolution, respectively, confirmed the formation of well-ordered disulfide bonds positioned to stabilize the binding loop. Long all-atom molecular dynamics simulations of disulfide-engineered Kunitz domains and their complexes with mesotrypsin revealed conformational stabilization of the primed side of the inhibitor-binding loop by the engineered disulfide, along with global suppression of conformational dynamics in the Kunitz domain. Our findings suggest that the Cys-17-Cys-34 disulfide slows proteolysis by dampening conformational fluctuations in the binding loop and minimizing motion at the enzyme-inhibitor interface. The generalizable approach developed here for the stabilization against proteolysis of Kunitz domains, which can serve as important scaffolds for therapeutics, may thus find applications in drug development.

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors-S/Ace2, Tmprss2, Cathepsins L and K, and Mpro-to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.