COCαDA - Large-Scale Protein Interatomic Contact Cutoff Optimization by Cα Distance Matrices - Lemos, Rafael P.; Mariano, Diego; Silveira, Sabrina A.; Melo-Minardi, Raquel C. de (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2024)
Contacts, defined as interand intramolecular interactions predicted computationally, are typically detected using Euclidean distance and atom types. However, traditional methods can be computationally expensive and limit scalability. We introduce COCαDA (Contact Optimization by Cα Distance Analysis), a novel method that incorporates domain knowledge of amino acids to optimize distance cutoffs, simplifying implementation and enhancing efficiency. COCαDA outperforms traditional methods such as all-against-all, static cutoff (SC), and Biopython’s NeighborSearch (NS), averaging 2.5x faster than SC and 6x faster than NS. COCαDA is well-suited for exploratory and large-scale analyses and is freely available at https://github.com/LBS-UFMG/COCaDA .