Evaluation Of The Target Druggablity Based On Network Analysis And Discovery Of Novel Inhibitors Against HFTase

Target identification and validation, as well as the lead compound discovery and optimization are key steps in the preliminary stage of drug discovery, with the development of omics, structural biology and computer technologies, Computer-Aided Drug Design (CADD) and network pharmacology an increasingly important role in this process. In this dissertation, we briefly introduced the key technologies of virtual screening and network pharmacology, and highlighted of virtual screening technology in the discovery of hFTase inhibitors and analysis of the druggablity of targets based on protein-protein interaction network.As the upstream of drug discovery, identification of poteintial drug targets is very important to the early-stage drug discovery, drug safety evaluation and old drugs with new applications. With continuously development of polypharmacology, perfection of network computational methods and disease-related databases, more network pharmacology methods have been used to analyze the druggability of targets. By mapping approved drug targets (ADTs), disease proteins and essential proteins to human protein-protein interaction network (PPIN), we obtained topological properties of different protein sets. Moreover, ADTs in PPIN were classified by drug Anatomical Therapeutic Chemical (ATC) system and cellular components. Based on the topological properties, SVM classification of ADTs and nondrug targets were performed, after5-fold cross-validation, we got an overall accuracy of0.76for antineoplastic ADTs and0.72for ADTs via extracellular location classification.hFTase is a critical post-translational modification enzyme, as well as a member of prenyltransferase. Monotherapy of hFTase inhibitors can be used for the treatment of tumor, while more attentions have been paid to the combinatorial therapy of hFTase inhibitors with other anticancer inhibitors. By mapping hFTase to protein-protein interaction network using the above target identification methods, we further analyzed and obtained the network properties of hFTase, and evaluated the druggablility of the target, which provides theoretical guidance for the design of multi-targets and combinatorial therapy. Based on structure-based virtual screening method, we discovered22novel compounds containing18new scaffolds as potential inhibitors from commercial databases, and the most potent inhibitor compound1showed an IC50value of11.9nM for hFTase. Meanwhile, these compounds showed moderate antiproliferative activity for MCF-7cell line, and compound15showed the most potent antiproliferative IC50values of12.23μM.