CavDetect: A DBSCAN Algorithm based Novel Cavity Detection Model on Protein Structure

TL;DR

This paper introduces CavDetect, a novel cavity detection model for protein structures that combines Voronoi Tessellation with the DBSCAN clustering algorithm to accurately identify ligand-binding sites crucial for drug design.

Contribution

The study presents a new cavity detection approach leveraging Voronoi Tessellation and DBSCAN, addressing the challenge of detecting cavities without prior knowledge of their number.

Findings

Effective detection of protein cavities demonstrated

DBSCAN handles dense atom data well

No prior knowledge of cavity count needed

Abstract

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.