Protein pocket detection via convex hull surface evolution and associated Reeb graph

Area of Science:

• Computational biology
• Structural bioinformatics
• Biophysics

Background:

• Protein pockets are vital for drug discovery, virtual screening, and understanding receptor-ligand interactions.
• Many proteins feature complex, hierarchical pockets capable of binding multiple ligands simultaneously.
• Existing methods lack the capability to analyze this intricate pocket-sub-pocket structure.

Purpose of the Study:

• To introduce a novel computational tool for detecting and analyzing hierarchical protein pocket structures.
• To provide a method for characterizing multi-ligand binding sites and their sub-pockets.
• To enable detailed analysis of protein binding site topology.

Main Methods:

• Utilizing differential geometry, algebraic topology, and physics-based simulations.
• Employing a convex hull surface evolution method governed by partial differential equations (PDEs).
• Applying surface evolution-induced Morse functions and Reeb graphs for structural characterization.

Main Results:

• The tool successfully detects protein pockets and characterizes their hierarchical sub-pocket organization.
• Validated on 4414 protein-ligand complexes from PDBbind, demonstrating robust performance.
• Accurate estimations of pocket surface area, volume, and depth are provided.

Conclusions:

• The developed computational tool offers a unique approach to analyzing complex protein pocket structures.
• Enables a deeper understanding of multi-ligand interactions within protein binding sites.
• Facilitates advancements in drug target identification and molecular design.