{"entity": "publication", "iuid": "f7862ba60ecb4483a95c7323c70138f9", "timestamp": "2026-09-30T05:04:15.816Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f7862ba60ecb4483a95c7323c70138f9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f7862ba60ecb4483a95c7323c70138f9"}}, "title": "GotEnzymes: an extensive database of enzyme parameter predictions.", "authors": [{"family": "Li", "given": "Feiran", "initials": "F", "orcid": "0000-0001-9155-5260", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/638ad58b8b28497a83955cea3c7dee17.json"}}, {"family": "Chen", "given": "Yu", "initials": "Y", "orcid": "0000-0003-3326-9068", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4d5b3f948bb84c05a28b5feeb3738128.json"}}, {"family": "Anton", "given": "Mihail", "initials": "M"}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33f2b49a39ed4e54ba77dfe397ed3087.json"}}], "type": "journal article", "published": "2023-01-06", "journal": {"title": "Nucleic Acids Res.", "issn": "1362-4962", "volume": "51", "issue": "D1", "pages": "D583-D586", "issn-l": "0305-1048"}, "abstract": "Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.", "doi": "10.1093/nar/gkac831", "pmid": "36169223", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9825421"}, {"db": "pii", "key": "6725766"}], "notes": [], "created": "2026-09-23T08:30:28.923Z", "modified": "2026-09-23T08:30:28.992Z"}