Fig. 1

Workflow for the identification and prioritization of therapeutic targets regulating metastatic melanoma phenotypes and virtual screening of repurposed drugs. The overall workflow is divided into four parts. (A) The E2F1 interaction map was used to derive positive/negative feedback loops followed by the calculation of node properties, network reduction via a multi-objective function, and subsequently merging the top-ranked motifs to generate a network core. (B) Melanoma-specific regulatory core: The constructed core consists of 183 direct interactions (edges) involving 34 core proteins and miRNAs, 10 receptor proteins, and 4 EMT marker proteins. Regulatory directions were retrieved from the E2F1 map as activation(+ 1), inhibition(-1), and unidentified(0). Logic-based model: The model is divided into three layers: the input layer containing receptor molecules (yellow color nodes), the middle layer comprising the regulatory network molecules (cyan color nodes) with known-marker proteins (pink color nodes), and the output layer containing the EMT phenotype (red color node). Green color edges represent activation, red color edges represent inhibition and gray edges represent neutral regulatory relationships among the nodes. (C) Two protein signatures (AKT1 and MDM2) were identified through the in silico perturbation experiments on the logic-based model. The functional binding sites of AKT1 and MDM2 are shown in the ribbon model with key amino acid residues participating in the binding pocket formation. At the bottom, the surface models of AKT1 and MDM2 are exposed. In the case of AKT1 (PDB: 3OCB_chainA) the kinase domain showing the ATP binding pocket is identified as the main binding pocket however, in the case of MDM2 (PDB: 3JZK_chain A) the binding site is identified as the main hydrophobic cavity that interacts with p53, displayed in yellow spheres respectively. (D) Virtual screening highlights various filtering steps for the identification of potential drug inhibitors from FDA-approved drug library