{
  "abstract": "Elucidating enzymatic reaction mechanisms requires a sequence of computational tasks comprising active-site extraction, minimum-energy-path search, transition-state (TS) refinement, intrinsic reaction coordinate (IRC) validation, and quasi-rigid-rotor harmonic-oscillator (QRRHO) thermochemistry─stages typically connected by ad hoc scripting and per-system tuning. We present pdb2reaction, an open-source Python command-line toolkit that automates this entire pipeline directly from a user-curated PDB using a single machine-learning interatomic potential (MLIP) backend. A GPU-accelerated pysisyphus fork is bundled to perform the Hessian-based heavy computations─Hessian, IRC, and vibrational analysis─on the same CUDA device as the MLIP for the default backend, minimizing data transfer overhead. This toolkit also implements a bond-change-driven recursive path-search algorithm, which recovered the two-step reaction mechanism of the geranyl pyrophosphate C6-methyltransferase BezA─an SN2-like methyl transfer followed by glutamate-mediated deprotonation via a cationic intermediate─locating both transition states and the intermediate itself without a user-supplied intermediate geometry: from a reactant-state active-site cluster and a minimal scan list, and independently from the reactant and product structures alone. On a benchmark of 23 reaction steps across six enzymes, the broadest-coverage backend, UMA-m-1.1, reproduced the literature mechanism for 19 steps, each with a single imaginary frequency at the transition state. For BezA, all five backends recovered the same two-step pathway and the same rate-limiting step. pdb2reaction enables rapid PDB-to-mechanism elucidation on a single GPU, providing a high-throughput complement to higher-cost DFT-based cluster-model calculations and full enzyme–solvent QM/MM calculations.",
  "abstract_shown": true,
  "affiliations": [
    "The University of Tokyo , , 1-1-1 Yayoi , Bunkyo-Ku, ,"
  ],
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  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
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        0
      ],
      "name": "Takuto Ohmura",
      "orcid": "0009-0008-1215-0168"
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        0
      ],
      "name": "Hajime Sato",
      "orcid": "0000-0001-5185-096X"
    },
    {
      "affs": [
        0
      ],
      "name": "Tohru Terada",
      "orcid": "0000-0002-7091-0646"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1xvc9a99py12z.bib",
  "cited_by": 2,
  "doi": "10.1021/acsomega.6c08242",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13717662/?tool=EBI",
  "id": "1xvc9a99py12z",
  "issns": [
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  "landing_url": "https://pubs.acs.org/acsodf/article/11/42/65454/5446631/pdb2reaction-End-to-End-Reaction-Path-Elucidation",
  "language": "eng",
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  "licence_label": "CC BY 4.0",
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  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c08242",
  "pmcid": "PMC13717662",
  "pmid": "42959874",
  "published": "2026-10-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/1xvc9a99py12z.ris",
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      "url": "https://pubs.acs.org/acsodf/article/11/42/65454/5446631/pdb2reaction-End-to-End-Reaction-Path-Elucidation"
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      "url": "https://europepmc.org/article/PMC/PMC13717662"
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    {
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      "url": "https://europepmc.org/article/MED/42959874"
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    {
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      "url": "https://doi.org/10.1021/acsomega.6c08242"
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  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Advanced Chemical Physics Studies",
      "subfield": "amo"
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  ],
  "title": "pdb2reaction : End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials",
  "title_text": "pdb2reaction : End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:56Z",
  "url": "https://research.draconitic.com/paper/1xvc9a99py12z",
  "venue": "ACS Omega",
  "versions": [
    {
      "doi": "10.26434/chemrxiv.15003538/v2",
      "pid": "665mtq4g3np9g",
      "relation": "preprint",
      "title": "pdb2reaction: End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials",
      "venue": "ChemRxiv"
    }
  ]
}