Pocket Concavity

Pocket Concavity refines predicted protein-ligand binding sites to produce accurate concavity shapes for structure-based drug design and ligand modeling.

Key Features:

• Alpha sphere-based refinement: Adjusts and trims pocket definitions derived from alpha sphere-based methods to mitigate overestimated pocket volumes and better match ligand-occupied volume.
• Dual operational modes (Ligand-Free and Ligand-Bound): Provides a Ligand-Free (LF) mode to identify and refine deep druggable concavities and a Ligand-Bound (LB) mode to refine binding sites around an existing ligand for interaction optimization.
• Structure-based drug design support: Supplies precise concavity shapes to inform identification and design of compounds in SBDD workflows.
• Implementation and dependencies: Implemented in Python3 and leverages Fpocket2 for pocket detection and alpha sphere computation.

Scientific Applications:

• Lead identification: Improves early-stage identification of viable lead compounds by refining predicted binding site geometries.
• Binding mode and interaction modeling: Enables more accurate modeling of protein–ligand interactions by aligning pocket shapes with ligand occupancy.
• Hit-to-lead optimization: Supports optimization of ligand chemistry by refining binding sites in ligand-bound contexts to guide interaction improvements.

Methodology:

Uses alpha sphere-based pocket detection via Fpocket2, with a Python3 implementation, and operates in ligand-free (LF) and ligand-bound (LB) modes to refine pocket concavities.