Predicting a pathogen's resistance mutations. Pictured at left is an illustration of a game between scientists and bacteria. For every drug that scientists develop against bacteria (a "move"), bacteria respond with mutations that confer resistance to the drug. In this paper, we show that these "moves' by bacteria can be predicted in silico ahead of time by the Osprey protein design algorithm. We used Osprey to prospectively predict in silico mutations in Staphylococcus aureus against a novel preclinical antibiotic, and validated their predictions in vitro and in resistance selection experiments. Image created for this paper by Lei Chen and Yan Liang (L2Molecule.com).
Significance. Computationally predicting drug resistance mutations early in the discovery phase would be an important breakthrough in drug development. The most meaningful predictions of target mutations will show reduced affinity for the drug while maintaining viability in the complex context of a cell. Here, the protein design algorithm, K* in Osprey, was used to predict a single nucleotide polymorphism in the target DHFR that confers resistance to an experimental antifolate in the preclinical discovery phase. Excitingly, the mutation was also selected in bacteria under antifolate pressure, confirming the prediction of a viable molecular response to external stress.
Abstract. Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first-generation drug candidates. In this study, a structure-based protein design algorithm, K* in the Osprey suite, was used to prospectively identify single nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance and the compensatory relationship of the mutations. To our knowledge, this is the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.