Statistical analysis and heuristic identification of unexpected interactions from the neurokinase‐inhibitor interactome in trigeminal neuralgia pharmacological intervention

Abstract

The cell signaling pathways of human trigeminal neuralgia (TN) are orchestrated by a variety of protein kinase‐elicited phosphorylation events. However, a majority of TN‐related neurokinases have not yet been revealed and still remain largely unexplored. Here, a systematic kinase‐inhibitor interactome for 601 small‐molecule inhibitors across 51 human neurokinases is created via a high‐throughput molecular docking procedure; the inhibitors are reversible, ATP competitive, and commercially available, while the kinases are enriched by gene ontology (GO) to be semantically relevant with TN and have cocrystallized structures with their cognate ligands. Heuristic clustering and statistical analysis identify 35 hit inhibitors from the interactome profile, which are potential to target TN‐related kinases. The intermolecular interactions of four promising inhibitors with the array of 51 TN‐related kinases are investigated in detail at molecular level using dynamics simulation and energetics analysis. Consequently, a total of nine protein kinases are inferred as high‐affinity cotargets of these potent inhibitors, in which fours have already been established as sophisticated targets for TN therapy, while others are still unclear about their therapeutic implications underlying the disease. Some candidates are selected as representatives to perform kinase assay. It is found that some putative kinase targets can be inhibited effectively by noncognate inhibitors; the activity values are comparable with or even better than known cognate inhibitors. Structural examination of a potent kinase‐inhibitor complex identifies a number of noncovalent interactions such as hydrogen bonds, cation‐π stacking, and hydrophobic contacts at the tightly packed complex interface, conferring both stability and specificity to the complex recognition and interaction.