CavDetect: A DBSCAN Algorithm based Novel Cavity Detection Model on Protein Structure
This addresses a domain-specific problem in computational biology for drug design, with incremental improvements in cavity detection methods.
The study tackled the problem of detecting cavities on protein structures, which are crucial for drug design, by proposing a novel cavity detection model based on Voronoi Tessellation and DBSCAN, achieving detection without prior knowledge of cavity numbers.
Cavities on the structures of proteins are formed due to interaction between proteins and some small molecules, known as ligands. These are basically the locations where ligands bind with proteins. Actual detection of such locations is all-important to succeed in the entire drug design process. This study proposes a Voronoi Tessellation based novel cavity detection model that is used to detect cavities on the structure of proteins. As the atom space of protein structure is dense and of large volumes and the DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm can handle such type of data very well as well as it is not mandatory to have knowledge about the numbers of clusters (cavities) in data as priori in this algorithm, this study proposes to implement the proposed algorithm with the DBSCAN algorithm.