SplitPocket

SplitPocket identifies functional surfaces on proteins from structural coordinates by detecting "split pockets"—pockets divided by bound ligands—to localize potential binding sites.

Key Features:

• Geometric Analysis Using Alpha Shape Theory: Applies Alpha Shape Theory to extract site-specific spatial information from protein coordinate data.
• Reduction of Search Space: Tailors probe radii according to physicochemical properties of molecules to reduce the computational search space for pocket detection.
• Advanced Geometric Measurements: Derives geometric measurements and spatial patterns using weighted Delaunay triangulation and discrete flow algorithms.
• Hydrophobicity Measurement: Assesses hydrophobicity on surface patches to characterize the physicochemical environment of predicted pockets.
• Evolutionary Conservation Quantification: Quantifies evolutionary conservation using an index derived from entropy scores in HSSP (homology-derived secondary structure of proteins).

Scientific Applications:

• Predicting Functional Surfaces: Applied to approximately 1.16 million potential pockets, it identified split pockets in more than 26,000 Protein Data Bank (PDB) structures, supporting prediction of functional surfaces on unbound proteins.
• Protein Functional Inference: Infers potential protein functions by correlating identified split pockets with structural features.
• Structural Evolution Studies: Analyzes evolutionary conservation of pockets to inform studies of protein structural evolution.
• Drug Design: Provides spatial patterns and physicochemical properties of binding sites to inform rational drug design.

Methodology:

Uses Alpha Shape Theory, tailored probe radii, weighted Delaunay triangulation, discrete flow algorithms, hydrophobicity assessment on surface patches, and an evolutionary conservation index derived from HSSP entropy scores.