Knowledge-Based Virtual Screening: Application to the MDM4/p53 ProteinProtein Interaction

Chemogenomics knowledge-based drug discovery approaches aim to extract the knowledge gained from one target and to apply it for the discovery of ligands and hopefully drugs of a new target which is related to the parent target by homology or conserved molecular recognition. Herein, we demonstrate the potential of knowledge-based virtual screening by applying it to the MDM4-p53 protein–protein interaction where the MDM2-p53 protein–protein interaction constitutes the parent reference system; both systems are potentially relevant to cancer therapy. We show that a combination of virtual screening methods, including homology based similarity searching, QSAR (Quantitative Structure–Activity Relationship) methods, HTD (High Throughput Docking), and UNITY pharmacophore searching provide a successful approach to the discovery of inhibitors. The virtual screening hit list is of the magnitude of 50,000 compounds picked from the corporate compound library of ∼1.2 million compounds. Emphasis is placed on the facts that such campaigns are only feasible because of the now existing HTCP (Highthroughput Cherry-Picking) automation systems in combination with robust MTS (Medium Throughput Screening) fluorescence-based assays. Given that the MDM2-p53 system constitutes the reference system, it is not surprising that significantly more and stronger hits are found for this interaction compared to the MDM4-p53 system. Novel, selective and dual hits are discovered for both systems. A hit rate analysis will be provided compared to the full HTS (High-throughput Screening).