pdb2reaction 0.3.5

pdb2reaction - Automated enzyme reaction path modeling from PDB structures

pdb2reaction: automated reaction-path modeling directly from PDB structures

Overview

pdb2reaction is a Python CLI toolkit for turning PDB structures into enzymatic reaction pathways with machine-learning interatomic potentials (MLIPs). Each workflow step is also available as an individual subcommand (opt, scan, scan2d, path-search, tsopt, freq, irc, dft, energy-diagram, etc.) for fine-grained control.

A single command can generate a first-pass enzymatic reaction path:

# bezA (GPP C6-methyltransferase): methyl transfer (SAM→GPP C6) + proton abstraction (E170)
pdb2reaction -i 1.R.pdb 3.P.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3'

# Scan mode (single structure → staged bond scans → MEP)
pdb2reaction -i 1.R.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --scan-lists '[("CS1 SAM 320","GPP 321 C7",1.60)]' \
                 '[("GPP 321 H11","GLU 186 OE2",0.90)]'

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The full workflow — MEP search → TS optimization → IRC → thermochemistry → single-point DFT — can be run in one command:

pdb2reaction -i 1.R.pdb 3.P.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --tsopt --thermo --dft

Working examples are provided in the examples/ directory, including complete all workflow scripts for both multi-structure MEP and scan-based pipelines. The example system is GPP C6-methyltransferase BezA (Tsutsumi et al., Angew. Chem. Int. Ed. 2022, 61, e202111217), which catalyzes a two-step reaction: (1) electrophilic methyl transfer from SAM to the C6 position of GPP via a C7 carbocation intermediate, and (2) proton abstraction from C6 by the catalytic base E170 to yield 6-methylgeranyl pyrophosphate (6MGPP).

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Given (i) two or more PDB files (R → ... → P), or (ii) one PDB with --scan-lists, or (iii) one TS candidate with --tsopt, pdb2reaction automatically:

• extracts an active-site model around user-defined substrates to build a cluster model,
• explores minimum-energy paths (MEPs) with GSM or DMF,
• optionally optimizes transition states, runs vibrational analysis, IRC, and single-point DFT,

using machine-learning interatomic potentials (MLIPs).

Related tools

Tool	Use case	Repository
mlmm-toolkit	ML/MM (ONIOM) with full protein environment — automates MM parameter generation and ML region assignment from a single PDB input	https://github.com/t-0hmura/mlmm_toolkit
UMA–Pysisyphus Interface	YAML-input-based reaction mechanism analysis for small molecules	https://github.com/t-0hmura/uma_pysis

Both pdb2reaction and mlmm-toolkit include a custom GPU-optimized pysisyphus fork for geometry optimization, TS search, and IRC. This bundled fork is not compatible with the upstream pysisyphus package; do not install them side by side.

Important (prerequisites):

• Input PDB files must already contain hydrogen atoms.
• When providing multiple PDBs, they must contain the same atoms in the same order (only coordinates may differ).
• Boolean CLI options accept both --flag / --no-flag and value style --flag True/False (yes/no, 1/0 are also accepted). Prefer toggle style in new scripts.
• The workflow also works for small-molecule systems. If you omit --center/-c and --ligand-charge, you can use .xyz or .gjf inputs as well.

Documentation

• Getting Started — Quick start and workflow overview
• Installation — Setup and dependency installation
• Examples — Working all workflow scripts (MEP and scan pipelines) for bezA
• YAML Reference — Configuration options
• JSON Output Reference — Machine-readable result.json schema
• Troubleshooting — Common errors, backend selection guide, VRAM requirements
• Full documentation: docs/index.md

This software is still under development. Please use it at your own risk.

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Installation

pdb2reaction requires Linux with a CUDA-capable GPU.

Prerequisites

• Python >= 3.11
• CUDA 12.x

Minimal setup (CUDA 12.9, torch 2.8.0)

pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu129
pip install pdb2reaction
plotly_get_chrome -y
huggingface-cli login

For DMF method

conda create -n pdb2reaction python=3.11 -y
conda activate pdb2reaction
conda install -c conda-forge cyipopt -y
pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cu129
pip install pdb2reaction
plotly_get_chrome -y

DFT single-point (pdb2reaction dft)

DFT dependencies are not installed by default. To use pdb2reaction dft, install the [dft] extra:

pip install "pdb2reaction[dft]"

This installs PySCF, GPU4PySCF (x86_64 only), and related CUDA libraries. Note that DFT single-point calculations are practical only for systems up to ~500 atoms; larger systems will require prohibitive compute time and memory.

For detailed installation instructions, see Installation.

Supported ML potentials

Potential	Repository	Install extra
UMA (default)	https://github.com/facebookresearch/fairchem	(included)
ORB	https://github.com/orbital-materials/orb-models	pip install "pdb2reaction[orb]"
MACE	https://github.com/ACEsuit/mace	See below
AIMNet2	https://github.com/isayevlab/aimnetcentral	pip install "pdb2reaction[aimnet]"

MACE installation: MACE requires e3nn==0.4.4, which conflicts with fairchem-core (UMA). To use MACE, first uninstall UMA's dependency, then install MACE:

pip uninstall fairchem-core
pip install mace-torch

UMA and MACE cannot coexist in the same environment. Use separate conda environments if you need both.

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Quick Examples

The examples below use GPP C6-methyltransferase BezA (Tsutsumi et al., Angew. Chem. Int. Ed. 2022, 61, e202111217) — a two-step mechanism: electrophilic methyl transfer from SAM to GPP C6 (via C7 carbocation), then proton abstraction by E170. Complete working scripts are in examples/.

Full workflow (multi-structure MEP)

pdb2reaction -i 1.R.pdb 3.P.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --tsopt --thermo --out-dir result_mep

Scan mode (single structure → staged bond scans → MEP)

pdb2reaction -i 1.R.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --scan-lists '[("CS1 SAM 320","GPP 321 C7",1.60)]' \
                 '[("GPP 321 H11","GLU 186 OE2",0.90)]' \
    --tsopt --thermo --out-dir result_scan

TS optimization only

pdb2reaction -i TS_candidate.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --tsopt

Step-by-step workflow

1. Extract active-site model (cluster model) — extract

pdb2reaction extract -i 1.R.pdb -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' -r 6.0

2. Optimize geometry — opt

pdb2reaction opt -i model.pdb -l 'SAM:1,GPP:-3'

3. MEP search — path-opt

pdb2reaction path-opt -i R_model.pdb P_model.pdb -l 'SAM:1,GPP:-3'

4. TS optimization — tsopt

pdb2reaction tsopt -i hei.pdb -l 'SAM:1,GPP:-3'

5. Frequency analysis — freq

pdb2reaction freq -i ts_optimized.pdb -l 'SAM:1,GPP:-3'

6. IRC — irc

pdb2reaction irc -i ts_optimized.pdb -l 'SAM:1,GPP:-3'

7. DFT single-point — dft

pdb2reaction dft -i optimized.pdb -l 'SAM:1,GPP:-3'

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CLI Subcommands

Workflow

Subcommand	Role	Documentation
all	End-to-end: extraction → MEP → TS → IRC → freq → DFT	docs/all.md

Structure Preparation

Subcommand	Role	Documentation
extract	Extract active-site model (cluster model)	docs/extract.md
fix-altloc	Resolve alternate conformations in PDB files	docs/fix_altloc.md
add-elem-info	Add/repair PDB element columns (77–78)	docs/add_elem_info.md

Optimization & Path Search

Subcommand	Role	Documentation
opt	Geometry optimization (L-BFGS or RFO)	docs/opt.md
tsopt	TS optimization (Dimer or RS-I-RFO)	docs/tsopt.md
path-opt	MEP optimization via GSM or DMF	docs/path_opt.md
path-search	Recursive MEP search with refinement	docs/path_search.md
scan	1D bond-length driven scan	docs/scan.md
scan2d	2D distance grid scan	docs/scan2d.md
scan3d	3D distance grid scan	docs/scan3d.md

Analysis

Subcommand	Role	Documentation
freq	Vibrational frequency analysis + thermochemistry	docs/freq.md
irc	IRC calculation (EulerPC)	docs/irc.md
dft	Single-point DFT (GPU4PySCF / PySCF)	docs/dft.md
bond-summary	Compare structures and report bond changes	docs/bond-summary.md

Visualization

Subcommand	Role	Documentation
trj2fig	Energy plot from XYZ trajectory	docs/trj2fig.md
energy-diagram	Energy diagram from numeric values	docs/energy_diagram.md

Tip: In tsopt, freq, and irc, setting --hessian-calc-mode Analytical is strongly recommended when you have enough VRAM.

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HPC / Multi-GPU

On HPC clusters or multi-GPU workstations, pdb2reaction can parallelize UMA inference across nodes. Set workers and workers_per_node to enable parallel inference; see docs/uma_pysis.md for details.

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Getting Help

pdb2reaction --help
pdb2reaction <subcommand> --help
pdb2reaction <subcommand> --help-advanced
pdb2reaction all --help-advanced
# Shorthand alias (equivalent to pdb2reaction)
p2r --help
# Equivalent module invocation
python -m pdb2reaction --help

pdb2reaction all --help shows core options. Use pdb2reaction all --help-advanced for the full option list. scan, scan2d, scan3d, and the calculation commands (opt, path-opt, path-search, tsopt, freq, irc, dft) now follow the same progressive-help pattern (--help core, --help-advanced full). add-elem-info, trj2fig, and energy-diagram also use the same pattern. extract and fix-altloc also support progressive help (--help core, --help-advanced full parser options).

If you encounter any issues, please open an issue at https://github.com/t-0hmura/pdb2reaction/issues.

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Citation

A preprint describing pdb2reaction is in preparation. Currently, if you find this work helpful for your research, please cite the software itself:

@software{ohmura2026pdb2reaction,
  author       = {Ohmura, Takuto},
  title        = {pdb2reaction},
  year         = {2026},
  month        = {3},
  version      = {0.3.2},
  url          = {https://github.com/t-0hmura/pdb2reaction},
  license      = {GPL-3.0},
  doi          = {10.5281/zenodo.19197878}
}

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Known limitations

• MACE and UMA cannot coexist in the same environment due to an e3nn version conflict. Use separate conda environments.
• DFT single-point (pdb2reaction dft) is practical up to ~500 atoms; larger systems may require fragmentation.
• ORB backend has a higher failure rate on multi-step reactions (SVD failures in path optimization).
• CPU-only execution is supported but 10-100x slower than GPU.

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License

pdb2reaction is distributed under the GNU General Public License version 3 (GPL-3.0).

This software is still under development. Please use it at your own risk.