title: Feature-similarity protein classifier as a ligand engineering tool.
abstract: Kinases have been often targeted in drug therapy aimed at blocking signaling pathways. However, the conservation of protein structure across homologs often leads to uncontrolled cross-reactivity. On the other hand, sticky packing defects in proteins are typically not conserved across homologs, making them ligand-anchoring sites potentially important to enhance selectivity. Thus, we introduce a hierarchical clustering of PDB-reported kinases according to packing differences. This kinome partitioning is highly correlated with proximity relations arising from the pharmacological profiling of kinases. A variable packing sensitivity is observed for individual drugs, with highly promiscuous ligands being the most insensitive to packing differences. Our classifier enables a strategy to design selective inhibitors.
authors.affiliation: School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA.
authors.forename: Sridhar
authors.initials: S
authors.lastname: Maddipati
authors.forename: Ariel
authors.initials: A
authors.lastname: Fernández
mesh_terms: D003198:Computer Simulation; D015195:Drug Design; D008024:Ligands; D008958:Models, Molecular; D010770:Phosphotransferases / Q000737:chemistry*; D011485:Protein Binding; D017434:Protein Structure, Tertiary
chemical_list: D008024:Ligands; D010770:Phosphotransferases