Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Molecular Modeling

Background:

• Protein cavities are vital for biological processes like protein-ligand binding.
• Analyzing protein 3D structures from Protein Data Bank (PDB) files enables cavity detection.
• Cavities include pockets, clefts, voids, and channels on protein surfaces.

Purpose of the Study:

• To survey and classify algorithmic approaches for protein cavity computation.
• To extend the taxonomy of geometric algorithms for cavity detection.
• To highlight GPU-accelerated techniques for enhanced performance.

Main Methods:

• Literature survey of protein cavity detection algorithms.
• Classification into evolution-based, energy-based, and geometry-based categories.
• Detailed examination of geometric methods (sphere, grid, tessellation, surface, hybrid, consensus, time-varying).

Main Results:

• A comprehensive classification of protein cavity detection algorithms.
• An extended taxonomy of geometric methods, including novel approaches.
• Identification of GPU-customized techniques for efficient computation.

Conclusions:

• Geometric algorithms are a key focus for protein cavity detection.
• The classification provides a framework for understanding diverse computational approaches.
• GPU computing offers significant speedups for these analyses in molecular graphics.