Targeting plague virulence factors: a combined machine learning method and multiple conformational virtual screening for the discovery of Yersinia protein kinase A inhibitors.

Yersinia spp. is currently an antibiotic resistance concern and a re-emerging disease. The essential virulence factor Yersinia protein kinase A (YpkA) contains a Ser/Thr kinase domain whose activity modulates pathogenicity. Here, we present an approach integrating a machine learning method, homology modeling, and multiple conformational high-throughput docking for the discovery of YpkA inhibitors. These first reported inhibitors of YpkA may facilitate studies of the pathogenic mechanism of YpkA and serve as a starting point for development of anti-plague drugs.