Targeting Plague Virulence Factors: A Combined Machine Learning Method and Multiple Conformational Virtual Screening for the Discovery ofYersiniaProtein Kinase A Inhibitors
Journal of Medicinal Chemistry
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. Figure 3. (A) Sequence alignment of YpkA (115-431) with protein kinases P38 and ERK. Strict sequence conservation is shown in red, and strong sequence conservation is shown in yellow. The solvent accessibility of each residue in the P38 structure is indicated in the bar at the base of the sequences, with white representing buried residues, dark-blue representing solvent-accessible residues, and light-blue representing an intermediate value. The secondary structural elements are also indicated according to the structure of P38. (B) Structural alignment of the two homology models of YpkA kinase domain. Model A (red) represents a conformation of YpkA with an open ATP-binding pocket, while model B (cyan) has a closed ATP-binding pocket. The key residues to the ligand binding are shown in magenta.