COC α DA - a fast and scalable algorithm for interatomic contact detection in proteins using C α distance matrices.

ABSTRACT: Protein interatomic contacts, defined by spatial proximity and physicochemical complementarity at atomic resolution, are fundamental to characterizing molecular interactions and bonding. Methods for calculating contacts are generally categorized as cutoff-dependent, which rely on Euclidean distances, or cutoff-independent, which utilize Delaunay and Voronoi tessellations. While cutoff-dependent methods are recognized for their simplicity, completeness, and reliability, traditional implementations remain computationally expensive, posing significant scalability challenges in the current Big Data era of bioinformatics. Here, we introduce COC α DA (COntact search pruning by C α Distance Analysis), a Python-based command-line tool for improving search pruning in large-scale interatomic protein

SUBMITTER: Lemos RP 

PROVIDER: S-EPMC12433948 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature