Dynamic‐shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket

Abstract

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer‐Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein‐ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand‐protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Drug Design by Pharmacophore and Virtual Screening Approach

“…However, this does not often happen and an alternative way to handle the flexibility consists in employing computational techniques such as flexible docking and/or MD simulations of the investigated system. Dynamic pharmacophores can be computed from protein-ligand MD trajectories and have been successfully applied in recent studies showing a better performance in the virtual screening of bioactive compounds compared to the classic rigid approach [ 54 , 55 , 56 ]. Nevertheless, this methodology needs higher computational resources (a problem that is less relevant due to the increasing availability of computing resources, and in case of application to a small set of selected ligands) and expert knowledge since it produces many models that should be averaged or clustered to derive more representative pharmacophores [ 57 ].…”