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 $\alpha$ DA (COntact search pruning by C $\alpha$ Distance Analysis), a Python-based command-line tool for improving search pruning in large-scale interatomic protein contact analysis using alpha-carbon (C $\alpha$ ) distance matrices. COC $\alpha$ DA detects intra- and inter-chain contacts, and classifies them into seven different types: hydrogen and disulfide bonds; hydrophobic effects; attractive, repulsive, and salt-bridge interactions; and aromatic stackings. To evaluate our tool, we compared it with three traditional approaches in the literature: all-against-all atom distance calculation ("brute-force"), static C $\alpha$ distance cutoff (SC), and Biopython's NeighborSearch class (NS). COC $\alpha$ DA demonstrated superior performance compared to the other methods, achieving on average 6x faster computation times than advanced data structures like k-d trees from NS, in addition to being simpler to implement and fully customizable. The presented tool facilitates exploratory and large-scale analyses of interatomic contacts in proteins in a simple and efficient manner, also enabling the integration of results with other tools and pipelines. The COC $\alpha$ DA tool is freely available at https://github.com/LBS-UFMG/COCaDA.