Prediction of estrogen receptor β ligands potency and selectivity by docking and MM-GBSA scoring methods using three different scaffolds

Abstract

This study aimed to identify the docking and molecular mechanics-generalized born surface area (MM-GBSA) re-scoring parameters which can correlate the binding affinity and selectivity of the ligands towards oestrogen receptor β (ERβ). Three different series of ERβ ligands were used as dataset and the compounds were docked against ERβ (protein data bank (PDB) ID: 1QKM) using Glide and ArgusLab. Glide docking showed superior results when compared with ArgusLab. Docked poses were then rescored using Prime-MM-GBSA to calculate free energy binding. Correlations were made between observed activities of ERβ ligands with computationally predicted values from docking, binding energy parameters. ERβ ligands experimental binding affinity/selectivity did not correlate well with Glide and ArgusLab score. Whereas calculated Glide energy (coulomb-van der Waal interaction energy) correlated significantly with binding affinity of ERβ ligands (r² = 0.66). MM-GBSA re-scoring showed correlation of r² = 0.74 with selectivity of ERβ ligands. These results will aid the discovery of novel ERβ ligands with isoform selectivity.

Keywords::

• MM-GBSA
• oestrogen receptor β
• anticancer
• free energy binding
• docking

Acknowledgment

The authors would also like to thank Dr. Madhavi Sastry and Dr. Shashidhar N. Rao from Schrödinger, India for their valuable suggestions and technical assistance.

Declaration of interest

The authors are grateful for the financial support from the Department of Science and Technology, SERC division India.