Pockets and region detection

Detecting binding pockets is an essential but complex task in computational biology. Molecular dynamics simulations, on the other hand, provide time-resolved insights into protein motion, revealing both persistent and transient pockets. However, the vast amount of data generated by these simulations necessitates the use of computational tools to systematically detect and analyze pockets, ensuring that relevant features are identified efficiently and accurately.

Convex hull

Detecting binding pockets on protein surfaces is a critical step in computational biology, particularly for drug discovery, ligand design, and protein-ligand interaction analysis. A convex hull-based approach is one geometric technique to identify pockets by enclosing the protein surface and iteratively evolving it inwards to delineate cavities and sub-cavities.

A convex hull is the smallest convex boundary that encloses a set of points. For protein pocket detection, this boundary initially encapsulates the entire protein surface. The inward evolution of this hull reveals potential binding pockets as it conforms to the protein's topology.

Initial convex hull construction

The initial construction of the convex hull is a critical step in pocket detection algorithms, as it serves as the starting boundary for identifying and characterizing potential binding sites on a protein. The convex hull represents the smallest convex structure that can enclose all atomic coordinates of the protein. This step is essential to reduce computational complexity and filter out irrelevant surface depressions that could be mistaken for functional pockets.

In this implementation, the convex hull is computed using the gift-wrapping algorithm, which iteratively identifies triangular facets of the hull by determining the most outward-facing points. Before performing this calculation, the Akl-Toussaint heuristic is applied to eliminate points that are guaranteed to be inside the hull. This heuristic approximates an octahedron encompassing the extremes of the protein structure and excludes interior points, significantly reducing the computational cost.

The convex hull itself is refined using only the alpha-carbon atoms of the protein backbone, which provides a simplified yet effective representation for identifying true pockets. This simplification minimizes noise from shallow surface contours and focuses on the broader structural features that are most relevant for pocket detection. The resulting convex hull provides the outer boundary for further analysis, allowing the algorithm to methodically refine pocket detection.