{"entity": "publication", "iuid": "63ff28dd59284918abbee948dbd2165d", "timestamp": "2026-08-29T04:16:06.825Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/63ff28dd59284918abbee948dbd2165d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/63ff28dd59284918abbee948dbd2165d"}}, "title": "PconsC4: fast, accurate and hassle-free contact predictions.", "authors": [{"family": "Michel", "given": "Mirco", "initials": "M"}, {"family": "Men\u00e9ndez Hurtado", "given": "David", "initials": "D"}, {"family": "Elofsson", "given": "Arne", "initials": "A"}], "type": "journal article", "published": "2019-08-01", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "volume": "35", "issue": "15", "pages": "2677-2679", "issn-l": "1367-4803"}, "abstract": "Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.\n\nHere, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.\n\nPconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.\n\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/bty1036", "pmid": "30590407", "labels": [], "xrefs": [{"db": "pii", "key": "5259184"}], "notes": [], "created": "2026-08-20T09:40:10.380Z", "modified": "2026-08-20T09:40:10.408Z"}