{
  "abstract": "Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost, leaving the field heavily dependent on expert intuition. Here we present a scalable physics-based framework that combines field-aware machine learning with molecular mechanics to capture enzyme electrostatics at quantum-mechanical accuracy while enabling efficient, atomistic exploration of reaction free-energy landscapes. Coupled with microkinetic modelling, the framework translates molecular free-energy landscapes into catalytic rates and selectivity across competing, multistep reaction pathways. Applied to a newly engineered oxidative amidase (OxiAm), the framework predicts catalytic rate constants with near-experimental accuracy, quantitatively resolves the selectivity between hydrolysis and aminolysis, and generalizes across substrates, mutations and enzyme homologues. Transition-state ensemble analysis further reveals the reaction mechanism and guides the design of enzyme variants for pharmaceutical synthesis. By bringing chemical accuracy and high-throughput sampling to enzyme catalysis, this approach shifts rational design from static, empirical practice toward dynamic, free-energy-driven design, and should accelerate the engineering of biocatalysts.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "name": "X. Wang"
    },
    {
      "name": "X. Qiu"
    },
    {
      "name": "Y. Wu"
    },
    {
      "name": "T. Niu"
    },
    {
      "name": "S. Zhang"
    },
    {
      "name": "R. Gao"
    },
    {
      "name": "I. Cho"
    },
    {
      "name": "H. Tang"
    },
    {
      "name": "K. Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Lei"
    },
    {
      "name": "O. Isayev"
    },
    {
      "name": "J. Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/anp2v42zqp6pn.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.15.751901",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751901v1.full",
  "id": "anp2v42zqp6pn",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751901v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1312|Enzyme Catalysis and Immobilization"
    ]
  ],
  "openalex": "W7213558948",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751901v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/anp2v42zqp6pn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:58Z",
      "native_id": "10.64898/2026.09.15.751901",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.15.751901v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "High-throughput physics-based enzyme engineering",
  "title_text": "High-throughput physics-based enzyme engineering",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:58Z",
  "url": "https://research.draconitic.com/paper/anp2v42zqp6pn",
  "venue": "bioRxiv",
  "versions": []
}