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Inhibitors of transcriptional protein–protein interactions (PPIs) have high value both as tools and for therapeutic applications. The PPI network mediated by the transcriptional coactivator Med25, for example, regulates stress-response and motility pathways, and dysregulation of the PPI networks contributes to oncogenesis and metastasis. The canonical transcription factor binding sites within Med25 are large (∼900 Å2) and have little topology, and thus, they do not present an array of attractive small-molecule binding sites for inhibitor discovery. Here we demonstrate that the depsidone natural product norstictic acid functions through an alternative binding site to block Med25–transcriptional activator PPIs in vitro and in cell culture. Norstictic acid targets a binding site comprising a highly dynamic loop flanking one canonical binding surface, and in doing so, it both orthosterically and allosterically alters Med25-driven transcription in a patient-derived model of triple-negative breast cancer. These results highlight the potential of Med25 as a therapeutic target as well as the inhibitor discovery opportunities presented by structurally dynamic loops within otherwise challenging proteins.

To overcome the Internet ossification, network virtualization has been proposed as a promising method because of its advantages (e.g., on demand and efficient resource allocation). Virtual network embedding (VNE) is one of the main challenges for network virtualization. Energy costs of servers in data centers (DCs) are major contribution to power consumption in information and communication technology (ICT). Therefore, VNE should consider both acceptance ratio and power consumption. In this paper, a mixed integer linear programming (MILP) model is proposed with the objective of minimizing the total power consumption in software defined optical data center networks (SD-ODCNs) by reducing the active data centers and power-consuming network components. In addition, the coordinates of nodes and delay of links are considered for more realistic scenario. Comparing with existing node ranking method, proposed global topology resource (GTR) can effectively evaluate the possibility of each DC node to host virtual nodes. Based on GTR method, we propose location aware energy efficient VNE algorithm, namely GTR-VNE. Simulation results show that GTR-VNE can obtain up to 9.3% and 5% improvement of power consumption and acceptance ratio compared with benchmarks. Furthermore, based on GTR and artificial intelligence ant colony optimization (ACO), another energy efficient algorithm ACO-VNE is proposed. ACO-VNE can obtain up to 28.7% improvement on power consumption compared with GTR-VNE. In addition, ACO-VNE has better performance in terms of revenue cost ratio and acceptance ratio.