Molecular Docking studies of D2 Dopamine receptor with Risperidone derivatives

Area of Science:

• Computational chemistry and molecular modeling.
• Pharmacology and drug discovery.
• Structural biology.

Background:

• The D2 dopamine receptor plays a crucial role in neurological functions and is a target for treating various disorders.
• Understanding ligand-receptor interactions is key for developing effective therapeutics.
• Existing models require validation and refinement for accurate drug design.

Purpose of the Study:

• To generate a validated 3D model of the D2 dopamine receptor.
• To investigate the binding interactions of Risperidone and its derivatives with the D2 receptor.
• To develop a Quantitative Structure-Activity Relationship (QSAR) model for predicting antagonist activity.

Main Methods:

• Comparative homology modeling using MODELLER to construct the D2 receptor 3D structure.
• Energy minimization and validation of the model using PROCHECK and Errat.
• Molecular docking simulations with AutoDock 4.2 against Risperidone and 15 derivatives.
• QSAR model development using Multiple Linear Regression with energy-based descriptors.

Main Results:

• A stable and validated 3D model of the D2 dopamine receptor was generated (PMDB-ID: PM0079251).
• Molecular docking provided energy-based descriptors for ligand-receptor interactions.
• A QSAR model was developed with a correlation coefficient (r2) of 0.513, indicating moderate predictive power.
• Cross-validation confirmed the predictive performance of the QSAR models.

Conclusions:

• The validated D2 dopamine receptor model serves as a basis for further drug design studies.
• QSAR modeling, combined with molecular docking, is a viable approach for predicting the activity of novel D2 receptor antagonists.
• This methodology can accelerate the discovery of new therapeutic agents targeting the D2 receptor.