{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:54:18Z","timestamp":1772240058707,"version":"3.50.1"},"reference-count":26,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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 COC\u03b1DA (Contact Optimization by C\u03b1 Distance Analysis), a novel method that incorporates domain knowledge of amino acids to optimize distance cutoffs, simplifying implementation and enhancing efficiency. COC\u03b1DA outperforms traditional methods such as all-against-all, static cutoff (SC), and Biopython\u2019s NeighborSearch (NS), averaging 2.5x faster than SC and 6x faster than NS. COC\u03b1DA is well-suited for exploratory and large-scale analyses and is freely available at https:\/\/github.com\/LBS-UFMG\/COCaDA.<\/jats:p>","DOI":"10.5753\/bsb.2024.245545","type":"proceedings-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T16:55:36Z","timestamp":1736441736000},"page":"59-70","source":"Crossref","is-referenced-by-count":4,"title":["COC\u03b1DA - Large-Scale Protein Interatomic Contact Cutoff Optimization by C\u03b1 Distance Matrices"],"prefix":"10.5753","author":[{"given":"Rafael P.","family":"Lemos","sequence":"first","affiliation":[]},{"given":"Diego","family":"Mariano","sequence":"additional","affiliation":[]},{"given":"Sabrina A.","family":"Silveira","sequence":"additional","affiliation":[]},{"given":"Raquel C. de","family":"Melo-Minardi","sequence":"additional","affiliation":[]}],"member":"3742","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Berman, H. 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