Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA

Area of Science:

• Entomology
• Molecular Biology
• Computational Chemistry

Background:

• The ecdysone receptor (EcR) is a key target for insecticides due to its role in insect molting.
• Synthetic EcR ligands offer potential for novel pest control strategies by disrupting insect growth.

Purpose of the Study:

• To develop and validate a computational method for predicting the binding activity of ecdysone receptor (EcR) ligands.
• To identify novel EcR-targeting compounds from a large chemical library for potential insecticide development.

Main Methods:

• Utilized the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method to predict ligand binding affinity.
• Performed validity analyses with known EcR ligands, incorporating ligand conformational free energy for improved accuracy.
• Applied structure-based hierarchical virtual screening to a database of 3.8 million compounds.

Main Results:

• The MM/PBSA method, with the conformational free energy term, accurately predicted EcR ligand binding.
• Virtual screening identified 12 candidate compounds from 3.8 million structures.
• Five of the selected compounds demonstrated activity in cell-based competitive binding assays.

Conclusions:

• The validated MM/PBSA approach is effective for virtual screening of EcR ligands.
• A proline derivative showed potent low micromolar binding activity, serving as a promising lead for new insecticides.
• This study provides a foundation for developing novel insect growth regulators targeting the ecdysone receptor.