{
  "abstract": "Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly for mutations at catalytically essential residues. Existing models often default to wild-type predictions even when mutations cause substantial activity loss. This limits their utility for enzyme design and mechanistic inference. Here, we introduce CatRange, a machine learning framework trained on CatLog-27k, a human-in-the-loop, AI-agent curated dataset of 27,176 in vitro enzyme-substrate kinetic records. CatLog-27k was created through systematic audit and correction of BRENDA and SABIO-RK, with mutant entries manually reconciled against 2,158 source articles. CatRange reframes kinetic prediction as classification into log₁₀-spaced bins for catalytic turnover (k cat ) and substrate affinity (KM ), rather than exact-value regression. This formulation reflects the order-of-magnitude scale at which enzyme kinetics are commonly interpreted. It reduces sensitivity to assay variability while preserving distinctions among functional catalytic and binding states. Using joint enzyme-substrate representations and gradient-boosted classifiers, CatRange predicts kinetic ranges for wild-type and mutant enzymes across held-out, out-of-distribution, and few-shot mutation settings. The model achieves robust order-of-magnitude recovery and class-balanced discrimination. Moreover, it captures mutation-induced transitions across kinetic regimes, including activity losses caused by perturbations of annotated catalytic residues. CatRange also identifies nonenzyme sequence inputs. Together, CatLog and CatRange provide an interpretable, mutation-sensitive framework for enzyme engineering and kinetic metabolic modeling.",
  "abstract_shown": true,
  "affiliations": [
    "Ames National Laboratory , Ames, IA 50011 ,",
    "Department of Chemical and Biological Engineering, Iowa State University , Ames, IA 50011 ,",
    "Srinivasa Ramanujan Institute for Basic Sciences , Pi Hills, 8th Mile, Velloor, Pampady, Kottayam, Kerala 686501 ,",
    "The Center for Biorenewable Chemicals, Iowa State University , Ames, IA 50011 ,",
    "Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln , Lincoln, NE 68588 ,",
    "Department of Computer Science and Engineering, University of South Florida , Tampa, FL 33620 ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Karuna Anna Sajeevan",
      "orcid": "0000-0002-9093-3845"
    },
    {
      "affs": [
        4
      ],
      "name": "Abraham Osinuga",
      "orcid": "0000-0002-2587-8704"
    },
    {
      "affs": [
        1
      ],
      "name": "B. Arunraj",
      "orcid": "0000-0002-1123-7906"
    },
    {
      "affs": [
        1
      ],
      "name": "Sakib Ferdous",
      "orcid": "0000-0001-7396-9830"
    },
    {
      "affs": [
        4
      ],
      "name": "Nabia Shahreen",
      "orcid": "0000-0002-6461-6184"
    },
    {
      "affs": [
        1
      ],
      "name": "Mohammed Sakib Noor",
      "orcid": "0000-0003-3120-0040"
    },
    {
      "affs": [
        1
      ],
      "name": "Shashank Koneru"
    },
    {
      "affs": [
        1
      ],
      "name": "Laura Mariana Santos-Correa",
      "orcid": "0009-0007-0353-4680"
    },
    {
      "affs": [
        1
      ],
      "name": "Rahil Salehi"
    },
    {
      "affs": [
        4
      ],
      "name": "Niaz Bahar Chowdhury",
      "orcid": "0009-0005-3833-6380"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Randy Aryee",
      "orcid": "0000-0002-8593-7702"
    },
    {
      "affs": [
        1
      ],
      "name": "Brisa Calderon-Lopez"
    },
    {
      "affs": [
        1
      ],
      "name": "Supantha Dey",
      "orcid": "0000-0003-1957-4613"
    },
    {
      "affs": [
        5
      ],
      "name": "Ankur Mali",
      "orcid": "0000-0001-5813-3584"
    },
    {
      "affs": [
        4
      ],
      "name": "Rajib Saha",
      "orcid": "0000-0002-2974-0243"
    },
    {
      "affs": [
        0,
        1,
        3
      ],
      "name": "Ratul Chowdhury",
      "orcid": "0000-0003-4522-6911"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/88npe201b6dkd.bib",
  "cited_by": 16,
  "doi": "10.1093/pnasnexus/pgag309",
  "fulltext_url": "https://europepmc.org/articles/PMC13659915",
  "id": "88npe201b6dkd",
  "issns": [
    "2752-6542"
  ],
  "landing_url": "https://europepmc.org/article/MED/42872055",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
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  "openalex": "W7213419845",
  "pdf_url": "https://europepmc.org/articles/PMC13659915?pdf=render",
  "pmcid": "PMC13659915",
  "pmid": "42872055",
  "published": "2026-09-16",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/88npe201b6dkd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42872055",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42872055"
    },
    {
      "first_seen": "2026-11-04T13:21:05Z",
      "native_id": "W7213419845",
      "source": "openalex",
      "url": "https://doi.org/10.1093/pnasnexus/pgag309"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "CatRange enables robust prediction of enzyme variant kinetic regimes.",
  "title_text": "CatRange enables robust prediction of enzyme variant kinetic regimes.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T13:20:46Z",
  "url": "https://research.draconitic.com/paper/88npe201b6dkd",
  "venue": "PNAS nexus",
  "versions": []
}