Melanoma inhibiting activity/cartilage-derived retinoic acid-sensitive protein (MIA/CD-RAP) is a small soluble protein secreted from malignant melanoma cells and from chondrocytes. Recently, we revealed that MIA/CD-RAP can modulate bone morphogenetic protein (BMP)2-induced osteogenic differentiation into a chondrogenic direction. In the current study we aimed to find the molecular details of this MIA/CD-RAP function. Direct influence of MIA on BMP2 by protein-protein-interaction or modulating SMAD signaling was ruled out experimentally. Instead, we revealed inhibition of ERK signaling by MIA/CD-RAP. This inhibition is regulated via binding of MIA/CD-RAP to integrin alpha5 and abolishing its activity. Active ERK signaling is known to block chondrogenic differentiation and we revealed induction of aggrecan expression in chondrocytes by treatment with MIA/CD-RAP or PD098059, an ERK inhibitor. In in vivo models we could support the role of MIA/CD-RAP in influencing osteogenic differentiation negatively. Further, MIA/CD-RAP-deficient mice revealed an enhanced calcified cartilage layer of the articular cartilage of the knee joint and disordered arrangement of chondrocytes. Taken together, our data indicate that MIA/CD-RAP stabilizes cartilage differentiation and inhibits differentiation into bone potentially by regulating signaling processes during differentiation.
Animals ; Bone Morphogenetic Proteins/metabolism ; Cartilage/*cytology/metabolism ; *Cell Differentiation ; Chondrocytes/cytology/enzymology ; Extracellular Matrix Proteins/deficiency/*metabolism ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Humans ; Integrin alpha5/metabolism ; Mesenchymal Stem Cells/cytology/metabolism ; Mice ; Neoplasm Proteins/deficiency/*metabolism ; Osteogenesis ; Protein Binding ; Signal Transduction ; Smad Proteins/metabolism

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the me-chanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52％and a specificity of 55％. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.