GeauxDock

GeauxDock predicts small-ligand binding conformations to pharmacologically relevant macromolecules to support structure-based drug discovery.

Key Features:

• Ligand Homology Modeling Approach: Applies ligand homology modeling to leverage evolutionary information from related structures for predicting binding conformations.
• Descriptor-Based Scoring Function: Employs a descriptor-based scoring function that integrates evolutionary constraints with physics-based energy terms.
• Mixed-Resolution Molecular Representation: Represents protein-ligand complexes at mixed resolutions to balance sampling efficiency and interaction detail.
• Efficient Monte Carlo Sampling Protocol: Uses an optimized Monte Carlo protocol to sample conformational space of protein-ligand complexes.
• Optimized Scoring for Native-Likeness: Calibrates scoring so that total pseudoenergy correlates with native-likeness of binding poses.
• Robust Performance Across Homology Levels: Demonstrates sustained accuracy when excluding closely related templates by increasing the contribution of physics-based energy terms at lower homology.

Scientific Applications:

• Structure-based Drug Discovery: Predicts ligand binding poses and identifies near-native conformations to aid lead design and optimization.
• Modeling Targets Across Homology Levels: Maintains predictive accuracy across varying template homology, enabling application to a wide range of molecular targets.

Methodology:

Combines ligand homology modeling, a descriptor-based scoring function integrating evolutionary constraints and physics-based energy terms, a mixed-resolution molecular representation, an optimized Monte Carlo sampling protocol, and scoring calibrated to correlate total pseudoenergy with native-likeness, with evaluation including exclusion of closely related templates.