Rational Design of Highly Potent and Slow-Binding Cytochrome bc1 Inhibitor as Fungicide by Computational Substitution Optimization

Hit to lead (H2L) optimization is a key step for drug and agrochemical discovery. A critical challenge for H2L optimization is the low efficiency due to the lack of predictive method with high accuracy. We described a new computational method called Computational Substitution Optimization (CSO) that has allowed us to rapidly identify compounds with cytochrome bc 1 complex inhibitory activity in the nanomolar and subnanomolar range. The comprehensively optimized candidate has proved to be a slow binding inhibitor of bc 1 complex, ~73-fold more potent ( K i = 4.1 nM) than the best commercial fungicide azoxystrobin (AZ; K i = 297.6 nM) and shows excellent in vivo fungicidal activity against downy mildew and powdery mildew disease. The excellent correlation between experimental and calculated binding free-energy shifts together with further crystallographic analysis confirmed the prediction accuracy of CSO method. To the best of our knowledge, CSO is a new computational approach to substitution-scanning mutagenesis of ligand and could be used as a general strategy of H2L optimisation in drug and agrochemical design.