{
  "abstract": "Enzyme function annotation is critical for decoding biological systems and enabling rational enzyme design. However, experimentally characterized enzyme functions remain available for only a small fraction of known protein sequences, resulting in a widening gap between rapidly expanding sequence space and reliable functional assignments. Recent advances in artificial intelligence (AI) have accelerated enzyme function annotation by facilitating accurate and high-throughput prediction from protein sequences. Here, we present CLEAN Database (CLEAN DB), a comprehensive resource encompassing >55 million enzyme function annotations across 1 098 305 strains, predicted by the CLEAN AI model. The database features an intuitive web interface that enables users to efficiently search, explore, and download enzyme function data through flexible query options. Additionally, powered by a protein language model, CLEAN DB supports mutation effect prediction through interactive heatmaps, structural overlays, and tabular data visualizations. We anticipate that CLEAN DB will serve as a valuable platform for advancing enzyme discovery and applications in synthetic biology, metabolic engineering, enzyme engineering, retrosynthesis, and drug discovery. The database is freely available at https://cleandb.platform.moleculemaker.org/.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,",
    "Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,",
    "NSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,",
    "Department of Electrical Engineering and Computer Sciences, University of California , Berkeley, CA 94720 ,",
    "School of Information Sciences, University of Illinois Urbana-Champaign , Champaign, IL 61820 ,",
    "National Center for Supercomputing Applications, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,",
    "NSF iBioFoundry, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,",
    "NSF Global Center for Biofoundry Applications, University of Illinois Urbana-Champaign , Urbana, IL 61801 ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Le Yuan",
      "orcid": "0000-0003-3317-9011"
    },
    {
      "affs": [
        3
      ],
      "name": "Jianan Canal Li"
    },
    {
      "affs": [
        4
      ],
      "name": "Miaoyuan Wen"
    },
    {
      "affs": [
        2,
        5,
        6
      ],
      "name": "David M Bianchi",
      "orcid": "0000-0003-4674-194X"
    },
    {
      "affs": [
        2,
        5,
        6
      ],
      "name": "Katherine Arneson",
      "orcid": "0009-0009-6114-241X"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Bingji Guo"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Sara Lambert"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Chris Stephens"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Tianhao Yu"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Lijiang Fu"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Christopher Pond"
    },
    {
      "affs": [
        2,
        5,
        6
      ],
      "name": "Matthew J Berry"
    },
    {
      "affs": [
        0,
        1,
        2,
        6,
        7
      ],
      "name": "Huimin Zhao",
      "orcid": "0000-0002-9069-6739"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gdgrswh52aw8.bib",
  "cited_by": 0,
  "doi": "10.1093/nar/gkag1003",
  "fulltext_url": null,
  "id": "1gdgrswh52aw8",
  "issns": [
    "0305-1048",
    "1362-4962"
  ],
  "landing_url": "https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1003/8916836",
  "language": null,
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7222013322",
  "pdf_url": "https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkag1003/71957356/gkag1003.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/1gdgrswh52aw8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:50Z",
      "native_id": "10.1093/nar/gkag1003",
      "source": "crossref",
      "url": "https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1003/8916836"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "CLEAN Database: providing enzyme function coverage for over 55 million sequences",
  "title_text": "CLEAN Database: providing enzyme function coverage for over 55 million sequences",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:50Z",
  "url": "https://research.draconitic.com/paper/1gdgrswh52aw8",
  "venue": "Nucleic Acids Research",
  "versions": []
}