pdb2reaction: End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials

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 S_N2-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.