Konformasyonel Dinamik Yönlendirmeli Farmakofor Modelleme ile Güçlü Antikanser Ajanlarının Belirlenmesi

Abstract

Targeting the interaction between tumor suppressor p53 and murine double minute 2(MDM2) has been an attractive therapeutic strategy of recent cancer research. There are a few number of MDM2-targeted anticancer drug molecules undergoing clinical trials, yet none of them have been approved so far. In this study, a new approach is employed in which dynamics of MDM2 obtained by elastic network models are used as a guide in the generation of the ligand-based pharmacophore model prior to virtual screening. Hit molecules exhibiting high affinity to MDM2 were captured and tested by rigid and induced-fit molecular docking. The knowledge of the binding mechanism was used while creating the induced-fit docking criteria. Application of Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method provided an accurate prediction of the binding free energy values. Two leading hit molecules which have shown better docking scores, binding free energy values and drug-like molecular properties were identified. These hits exhibited extra intermolecular interactions with MDM2, indicating a stable complex formation and hence would be further tested in vitro. Finally, the combined computational strategy employed in this study can be a promising tool in drug design for the discovery of potential new hits.