Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles

Abstract

Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling.