MDAnalysis-GROMACS

An integrated Python toolkit for GROMACS molecular dynamics simulation preparation and trajectory analysis. Covers the full pipeline — from raw protein structure to publication-quality figures — in a single, reproducible Python script.

The package has two independent modules that can be used together or separately:

sim_prep — automates GROMACS simulation setup for apo proteins, protein–ligand complexes, and mixed-solvent (MixMD) simulations
GromacsAnalysis — wraps 16 GROMACS analysis commands and extends them with pure-Python DCCM, PCA, free energy landscape, graph-theoretic network analysis, and PDB structure annotation

Features

Simulation preparation (`sim_prep`)

Class	Simulation type	Key steps
`ApoSimPrepper`	Protein-only	PDB cleaning → pdb2gmx → solvation → equilibration → production
`CplxSimPrepper`	Protein–ligand complex	3 input paths → GAFF parameterisation → topology assembly → equilibration
`MixMDPrepper`	Mixed-solvent (MixMD)	Multi-probe parameterisation → insert-molecules → equilibration

YAML / JSON config file support — drive the full pipeline from a plain text file
Persistent HPC checkpointing — resume interrupted pipelines from the last completed step
Native Python ligand parameterisation (Open Babel → antechamber → parmchk2 → tleap → ACPYPE)
Structure format conversion: .pdb, .cif / .mmcif, .gro, .mol2, .sdf, SMILES strings

Trajectory analysis (`GromacsAnalysis`)

Category	Method	Description
Trajectory prep	`nopbc_and_fit()`	Remove PBC, least-squares rot+trans fit
Essential dynamics	`essential_dynamics()`	RMSD, RMSF, radius of gyration
H-bond analysis	`hbond_analysis()`	H-bond count over time + per-pair occupancy
H-bond plot	`plot_hbond_occupancy()`	Occupancy bar chart + time series
SASA	`sasa_analysis()`	Total and per-residue solvent accessible surface area
SASA plot	`plot_sasa()`	SASA time series + per-residue colour-mapped bar chart
Covariance / PCA	`covariance_analysis()`	Full `gmx covar` wrapper
DCCM	`covariance_to_correlation()`	Covariance → normalised cross-correlation matrix
DCCM plot	`plot_dccm()`	Heatmap with configurable colourmap and tick spacing
Network	`correlation_network()`	Weighted residue graph + 10 graph-theoretic metrics
Network plot	`plot_correlation_network()`	Degree-centrality-coloured spring layout
PC projections	`project_pca()`	`gmx anaeig` → tidy CSV
Free energy	`free_energy_landscape()`	2-D FEL via Boltzmann inversion + Gaussian smoothing
FEL plot	`plot_free_energy_3d()`	3-D surface + gradient magnitude heatmap with contours
PDB colour	`colour_pdb_by_rmsf()`	B-factor ← RMSF
PDB colour	`colour_pdb_by_centrality()`	B-factor ← degree / betweenness / closeness / eigenvector

Repository Structure

mdanalysis-gromacs/
│
├── README.md                        # This file
├── CHANGELOG.md                     # Version history
├── CONTRIBUTING.md                  # Development guide and PR checklist
├── LICENSE                          # MIT licence
├── LIGAND_PARAMETERISATION.md       # AmberTools + ACPYPE setup guide
├── STRUCTURE.md                     # Detailed repo layout and design notes
├── pyproject.toml                   # PEP 517/518 build config; pip install .
├── requirements.txt                 # Tiered dependency list
│
├── docs/
│   ├── paper.md                     # JOSS manuscript
│   └── paper.bib                    # BibTeX references
│
├── gromacs_analysis.py              # Post-simulation analysis
│   └── GromacsAnalysis              # Single class — all analysis methods
│       ├── nopbc_and_fit()
│       ├── essential_dynamics()
│       ├── hbond_analysis() / plot_hbond_occupancy()
│       ├── sasa_analysis() / plot_sasa()
│       ├── covariance_analysis()
│       ├── covariance_to_correlation() / plot_dccm()
│       ├── correlation_network() / plot_correlation_network()
│       ├── project_pca()
│       ├── free_energy_landscape() / plot_free_energy_3d()
│       └── colour_pdb_by_rmsf() / colour_pdb_by_centrality()
│
├── sim_prep/                        # Simulation preparation package
│   ├── __init__.py                  # Exports ApoSimPrepper, CplxSimPrepper, MixMDPrepper
│   ├── base.py                      # SimulationPrepper — abstract base class
│   │                                #   shared steps: pdb2gmx, editconf, solvate,
│   │                                #   genion, minimise, nvt, npt, production
│   ├── apo.py                       # ApoSimPrepper — protein-only simulations
│   ├── complex.py                   # CplxSimPrepper — protein–ligand complexes
│   │                                #   Path A: AutoDock complex PDB
│   │                                #   Path B: native PDB/mmCIF + ligand file/SMILES
│   │                                #   Path C: pre-separated protein + ligand files
│   ├── mixmd.py                     # MixMDPrepper — mixed-solvent simulations
│   ├── config.py                    # YAML/JSON config loader + template generator
│   └── checkpoint.py               # CheckpointMixin — HPC resume support
│
├── utils/                           # Internal utilities
│   ├── __init__.py
│   ├── amber_params.py              # AmberParameteriser — ligand parameterisation
│   │                                #   obabel → antechamber → parmchk2 → tleap → ACPYPE
│   └── structure_io.py              # prepare_structure() — format conversion
│                                    #   .pdb .cif .mmcif .gro .mol2 .sdf smiles:
│
├── config/
│   ├── gmx/                         # MDP template files (not committed — user-provided)
│   │   ├── apo/                     # ions.mdp  em.mdp  nvt.mdp  npt.mdp  md.mdp
│   │   ├── complex/                 # ions_prot_lig.mdp  em_prot_lig.mdp  ...
│   │   └── mixmd/                   # ions_mix.mdp  em_mix.mdp  ...
│   └── examples/                    # Annotated YAML reference configs
│       ├── apo.yaml
│       ├── complex.yaml
│       └── mixmd.yaml
│
├── examples/                        # Working example scripts
│   ├── apo_example.py               # Full apo pipeline
│   ├── complex_example.py           # All three complex input paths
│   └── mixmd_example.py             # MixMD with multiple probes
│
└── tests/
    └── test_suite.py                # 53 tests across 14 classes

Note on config/: MDP template files contain force-field and hardware-specific parameters but those committed to the repository may not be set up for your system. Populate this directory with your own templates. See config/examples/ for the expected file names and CONTRIBUTING.md for the recommended MDP parameter values.

Requirements

External software

Tool	Version	Required for
GROMACS	≥ 2019	All simulation and analysis steps
AmberTools	≥ 23	Ligand parameterisation (`antechamber`, `parmchk2`, `tleap`)
ACPYPE	≥ 2022.7	Amber → GROMACS topology conversion
Open Babel	≥ 3.1	Ligand protonation

AmberTools, ACPYPE, and Open Babel are only required for CplxSimPrepper and MixMDPrepper. See LIGAND_PARAMETERISATION.md for full installation instructions.

Python

Python ≥ 3.10

Core dependencies (always required):

biopython  matplotlib  networkx  numpy  pandas  pyyaml  scipy

Structure handling (required for non-PDB inputs):

gemmi  rdkit

Best installed via conda: conda install -c conda-forge rdkit gemmi

Installation

From source (recommended)

git clone https://github.com/DoctorDean/GROMACS-Analysis.git
cd mdanalysis-gromacs
pip install -e .

The -e flag installs in editable mode so changes to the source are reflected immediately without reinstalling.

Core dependencies only

pip install -e .

With structure handling support

pip install -e ".[structure]"
# Or via conda for RDKit:
conda install -c conda-forge rdkit gemmi
pip install -e .

For development

pip install -e ".[dev]"

GROMACS is not installed via pip. Install it via your system package manager, HPC module system, or follow the official installation guide.

Quick Start

Analysis only (existing trajectory)

from gromacs_analysis import GromacsAnalysis

analysis = GromacsAnalysis(
    md_name="md_production",    # stem of your .tpr / .xtc files
    protein_name="MyProtein",   # used to label all output files
    work_dir="/path/to/sim",    # directory containing simulation files
)

analysis.nopbc_and_fit()
analysis.essential_dynamics(time_unit="ns")
analysis.hbond_analysis()
analysis.sasa_analysis()

Preparation from a YAML config file

# Generate an annotated template
python -c "from sim_prep.config import generate_template; generate_template('apo', 'my_sim.yaml')"

# Edit my_sim.yaml, then run:
python -c "from sim_prep.config import load_config; load_config('my_sim.yaml', run_all=True)"

Simulation Preparation

Configuration Files

Every simulation type can be fully configured from a YAML or JSON file. Annotated templates are provided in config/examples/.

from sim_prep.config import load_config, generate_template

# Write an annotated template to disk
generate_template("apo",     "apo_sim.yaml")
generate_template("complex", "complex_sim.yaml")
generate_template("mixmd",   "mixmd_sim.yaml")

# Load and run
sim = load_config("apo_sim.yaml")              # returns validated prepper object
sim = load_config("apo_sim.yaml", run_all=True)  # runs full pipeline immediately

A minimal apo config:

type: apo
protein_name: hsp90
sim_len: 100
bx_shp: dodecahedron
bx_dim: 1.0
md_name: md_production
pos_ion: NA
neg_ion: CL
work_dir: /data/simulations/hsp90_apo

Apo Simulations

from sim_prep import ApoSimPrepper

sim = ApoSimPrepper(
    protein_name="hsp90",
    sim_len=100,
    bx_dim=1.0,
    bx_shp="dodecahedron",
    md_name="md_production",
    work_dir="/data/simulations/hsp90_apo",
)
sim.validate_config()
sim.assign_attributes()
sim.copy_config_files()
sim.update_config_files()

sim.clean_pdb_file()
sim.protein_pdb2gmx()
sim.set_new_box()
sim.solvate()
sim.minimise_system()
sim.nvt_equilibration()
sim.npt_equilibration()
sim.production_run()

Protein–Ligand Complex

Three input paths are supported — choose the one that matches your upstream workflow:

from sim_prep import CplxSimPrepper

sim = CplxSimPrepper(
    protein_name="hsp90_ATP",
    ligand_code="ATP",
    sim_len=100,
    bx_dim=1.0,
    bx_shp="dodecahedron",
    md_name="md_production",
    work_dir="/data/simulations/hsp90_ATP",
)
sim.validate_config()
sim.assign_attributes()
sim.copy_config_files()
sim.update_config_files()

Path A — AutoDock / Smina docked complex:

sim.process_autodocked_complex()   # split protein + ligand, run pdb2gmx

Path B — Native PDB / mmCIF + separate ligand:

sim.prepare_from_structure(
    protein_input="6lu7.cif",        # .pdb / .cif / .mmcif / .gro
    ligand_input="N3.sdf",           # .pdb / .mol2 / .sdf / smiles:CC...
    keep_residues=["ZN"],            # retain metal ions
)

Path C — Pre-separated files (Glide, manual prep):

sim.prepare_from_separate_files(
    protein_file="protein_prepared.pdb",
    ligand_file="ligand_glide.sdf",
)

All paths continue identically from here:

sim.param_with_amber(net_charge=-4)  # net_charge is critical for charged ligands
sim.gro2itp()
sim.generate_ligand_ndx()
sim.build_gmx_complex()
sim.build_complex_topology()
sim.solvate()
sim.minimise_system()
sim.make_prot_lig_ndx()
sim.nvt_equilibration()
sim.npt_equilibration()
sim.production_run()

Upstream tool	Path	Input
AutoDock Vina / Smina	A	Docked complex PDB
RCSB PDB / AlphaFold	B	`.cif` + ligand `.sdf` or SMILES
Schrödinger Glide	C	Separate protein + ligand SDF
Virtual screening hit	B	Protein `.pdb` + SMILES string

Mixed-Solvent MixMD

from sim_prep import MixMDPrepper

sim = MixMDPrepper(
    protein_name="hsp90",
    sim_len=100,
    bx_dim=1.5,
    bx_shp="dodecahedron",
    md_name="md_production",
    work_dir="/data/simulations/hsp90_mixmd",
    ligands=[
        {"code": "ACT", "number": 50, "smiles": "CC(=O)[O-]"},
        {"code": "BNZ", "number": 20, "smiles": "c1ccccc1"},
    ],
)
sim.validate_config()
sim.assign_attributes()
sim.copy_config_files()
sim.update_config_files()
sim.clean_pdb_file()
sim.protein_pdb2gmx()
sim.set_new_box()
sim.param_all_ligands(net_charges={"ACT": -1, "BNZ": 0})
sim.top2itp()
sim.merge_atomtypes()
sim.build_mixmd()
sim.solvate()
sim.minimise_system()
sim.nvt_equilibration()
sim.npt_equilibration()
sim.production_run()

HPC Checkpointing

On HPC clusters where jobs have wall-time limits, the CheckpointMixin allows a pipeline to resume from the last completed step rather than restarting from scratch.

from sim_prep.checkpoint import CheckpointMixin
from sim_prep import ApoSimPrepper

class CheckpointedApo(CheckpointMixin, ApoSimPrepper):
    pass

sim = CheckpointedApo(
    protein_name="hsp90",
    sim_len=100,
    bx_dim=1.0,
    bx_shp="dodecahedron",
    md_name="md_production",
    work_dir="/data/simulations/hsp90_apo",
)
sim.validate_config()
sim.assign_attributes()
sim.resume_from_checkpoint()   # loads prior state; completed steps are skipped

sim.clean_pdb_file()           # skipped if already done
sim.protein_pdb2gmx()          # skipped if already done
sim.set_new_box()              # resumes from here if this was the failure point
sim.solvate()
sim.minimise_system()
sim.nvt_equilibration()
sim.npt_equilibration()
sim.production_run()

State is written to .sim_checkpoint.json in work_dir after every completed step. To check status or reset:

sim.checkpoint_status()            # print completed / pending steps
sim.reset_checkpoint(["solvate"])  # re-run a specific step
sim.reset_checkpoint()             # full reset

Trajectory Analysis

Essential Dynamics

from gromacs_analysis import GromacsAnalysis, SelectionGroups

groups = SelectionGroups(
    center="1",         # Protein — centering group
    output="0",         # System  — output group
    fit="4",            # Backbone — for rot+trans fitting
    rmsf="4",           # Backbone — RMSF per residue
    rg="1",             # Protein  — radius of gyration
    covar_fit="4",
    covar_analysis="4",
)

analysis = GromacsAnalysis(
    md_name="md_production",
    protein_name="HSP90",
    work_dir="/data/simulations/hsp90_apo",
    groups=groups,
)

analysis.nopbc_and_fit()
analysis.essential_dynamics(time_unit="ns")

Hydrogen Bond Analysis

# Intra-protein H-bonds
analysis.hbond_analysis(donor_group="Protein", acceptor_group="Protein")

# Protein–ligand interface H-bonds
analysis.hbond_analysis(donor_group="Protein", acceptor_group="ATP")

# Plot occupancy bar chart + time series
analysis.plot_hbond_occupancy(top_n=20, occupancy_threshold=0.1)

Outputs: hbond_num_{name}.xvg, hbond_matrix_{name}.xpm, hbond_occupancy_{name}.csv, hbond_occupancy_{name}.png, hbond_timeseries_{name}.png

SASA Analysis

analysis.sasa_analysis(group="Protein", probe_radius=0.14)
analysis.plot_sasa()

Outputs: sasa_total_{name}.xvg, sasa_residue_{name}.csv, sasa_timeseries_{name}.png, sasa_residue_{name}.png

Covariance and DCCM

analysis.covariance_analysis(time_unit="ns", last=10)
corr = analysis.covariance_to_correlation()
analysis.plot_dccm(corr=corr, cmap="bwr")

Correlation Network

G = analysis.correlation_network(corr=corr, threshold=0.3)
analysis.plot_correlation_network(G)

Printed metrics: connectivity, average path length, average clustering, max degree / betweenness / closeness / eigenvector centrality, max edge betweenness, degree assortativity, k-core size, articulation points.

Free Energy Landscape

pc_data   = analysis.project_pca(first=1, last=2, time_unit="ns")
landscape = analysis.free_energy_landscape(
    pc_data=pc_data,
    bin_width=2.0,
    sigma=1.5,
    temperature=300.0,
)
analysis.plot_free_energy_3d(landscape, cmap_G="viridis", cmap_grad="plasma")

PDB Colouring

# Colour by per-residue flexibility
analysis.colour_pdb_by_rmsf(pdb_file="average_structure.pdb")

# Colour by network centrality
for metric in ["degree", "betweenness", "closeness", "eigenvector"]:
    analysis.colour_pdb_by_centrality(
        pdb_file="average_structure.pdb",
        graph=G,
        metric=metric,
    )

Open in PyMOL: spectrum b, blue_white_red Open in ChimeraX: color bfactor palette blue:white:red

Multi-System Comparison

Each GromacsAnalysis instance is self-contained, making side-by-side comparisons straightforward:

systems = {
    "apo":  GromacsAnalysis("md_production", "HSP90_apo",  work_dir="/data/apo"),
    "holo": GromacsAnalysis("md_production", "HSP90_holo", work_dir="/data/holo"),
}

for name, sim in systems.items():
    sim.nopbc_and_fit()
    sim.essential_dynamics()
    sim.hbond_analysis()
    sim.sasa_analysis()
    corr = sim.covariance_to_correlation()
    G    = sim.correlation_network(corr=corr)
    pc   = sim.project_pca()
    land = sim.free_energy_landscape(pc_data=pc, temperature=300.0)
    sim.plot_free_energy_3d(land)
    sim.colour_pdb_by_centrality(graph=G, metric="betweenness")

API Reference

`SelectionGroups`

Dataclass holding GROMACS selection group indices for each analysis step. All fields default to standard protein-in-water indices.

Field	Default	Group	Used by
`center`	`"1"`	Protein	`nopbc_and_fit` — centring
`output`	`"0"`	System	`nopbc_and_fit` — output
`fit`	`"1"`	Protein	`nopbc_and_fit`, `essential_dynamics`
`rmsf`	`"4"`	Backbone	`essential_dynamics`
`rg`	`"1"`	Protein	`essential_dynamics`
`covar_fit`	`"4"`	Backbone	`covariance_analysis`
`covar_analysis`	`"4"`	Backbone	`covariance_analysis`

`GromacsAnalysis`

Parameter	Type	Default	Description
`md_name`	`str`	—	Stem of `.tpr` / `.xtc` files
`protein_name`	`str`	—	Label used in all output filenames
`work_dir`	`str \| Path`	`cwd`	Directory containing simulation files
`groups`	`SelectionGroups`	defaults	GROMACS selection group indices
`index_file`	`str`	`None`	Path to custom `.ndx` file
`gmx_executable`	`str`	`"gmx"`	GROMACS binary name or full path

Full method signatures and parameter documentation are available via:

help(GromacsAnalysis.hbond_analysis)
help(GromacsAnalysis.free_energy_landscape)
# etc.

`sim_prep` classes

Class	Required kwargs	Key optional kwargs
`ApoSimPrepper`	`protein_name`, `sim_len`, `bx_dim`, `bx_shp`, `md_name`	`pos_ion`, `neg_ion`, `work_dir`, `gmx_executable`, `index_file`
`CplxSimPrepper`	+ `ligand_code`	+ `param_ligand_name`, `remove_ligands`, `net_charge`, `atom_type`
`MixMDPrepper`	+ `ligands` (list of dicts)	+ `net_charges` (dict)

Output Files

Analysis outputs

File	Method	Description
`{md}_noPBC.xtc`	`nopbc_and_fit`	PBC-corrected trajectory
`{md}_fitted.xtc`	`nopbc_and_fit`	Fitted trajectory
`rmsd_{name}.xvg`	`essential_dynamics`	Backbone RMSD vs time
`rmsf_{name}.xvg`	`essential_dynamics`	Per-residue RMSF
`rg_{name}.xvg`	`essential_dynamics`	Radius of gyration vs time
`average_structure_{name}.pdb`	`essential_dynamics`	Time-averaged structure
`hbond_num_{name}.xvg`	`hbond_analysis`	H-bond count vs time
`hbond_matrix_{name}.xpm`	`hbond_analysis`	H-bond existence matrix
`hbond_occupancy_{name}.csv`	`hbond_analysis`	Per-pair occupancy table
`hbond_occupancy_{name}.png`	`plot_hbond_occupancy`	Occupancy bar chart
`hbond_timeseries_{name}.png`	`plot_hbond_occupancy`	H-bond count over time
`sasa_total_{name}.xvg`	`sasa_analysis`	Total SASA vs time
`sasa_residue_{name}.csv`	`sasa_analysis`	Per-residue mean SASA
`sasa_timeseries_{name}.png`	`plot_sasa`	Total SASA over time
`sasa_residue_{name}.png`	`plot_sasa`	Per-residue bar chart
`covar_{name}.xvg`	`covariance_analysis`	Eigenvalues
`eigenvec_{name}.trr`	`covariance_analysis`	Eigenvectors
`covar_{name}.dat`	`covariance_analysis`	ASCII covariance matrix
`cross_corr_{name}.csv`	`covariance_to_correlation`	DCCM matrix
`cross_corr_{name}.jpeg`	`plot_dccm`	DCCM heatmap
`network_{name}.png`	`plot_correlation_network`	Weighted network figure
`proj_PC{i}_{name}.xvg`	`project_pca`	Per-PC projection time series
`pc_projections_{name}.csv`	`project_pca`	Tidy multi-PC CSV
`fel_3d_{name}.png`	`plot_free_energy_3d`	3-D free energy surface
`fel_gradient_{name}.png`	`plot_free_energy_3d`	Gradient magnitude figure
`bfactor_rmsf_{name}.pdb`	`colour_pdb_by_rmsf`	RMSF-coloured PDB
`bfactor_{metric}_{name}.pdb`	`colour_pdb_by_centrality`	Centrality-coloured PDB

Checkpoint file

File	Description
`.sim_checkpoint.json`	Pipeline state written to `work_dir`; read by `resume_from_checkpoint()`

Visualising Results

PyMOL

# RMSF or centrality-coloured PDB
spectrum b, blue_white_red, minimum=0    # RMSF (low=blue, high=red)
spectrum b, yellow_green_blue            # centrality (low=yellow, high=blue)

ChimeraX

open bfactor_rmsf_MyProtein.pdb
color bfactor palette blue:white:red

DCCM — interactive inspection

import numpy as np
import matplotlib.pyplot as plt

data = np.loadtxt("cross_corr_MyProtein.csv")
plt.imshow(data, cmap="bwr", vmin=-1, vmax=1, origin="lower")
plt.colorbar(label="Cross-correlation")
plt.xlabel("Residue")
plt.ylabel("Residue")
plt.tight_layout()
plt.show()

Free energy landscape — custom plotting

The dict returned by free_energy_landscape() exposes the raw arrays directly:

land = analysis.free_energy_landscape(pc_data=pc)
# land["G"]          — (M, N) smoothed free energy array (kJ/mol)
# land["grad"]       — (M, N) gradient magnitude array
# land["X"], ["Y"]   — meshgrid of bin centres
# land["basin_pc1"]  — PC1 coordinate of the global minimum
# land["basin_G"]    — free energy at the global minimum

Testing

# Run all tests
pytest tests/ -v

# Run only pure-Python tests (no GROMACS required)
pytest tests/ -v -m "not integration"

# Run with coverage report
pytest tests/ --cov=. --cov-report=term-missing

The test suite contains 53 tests across 14 classes. Tests requiring GROMACS (@requires_gmx), RDKit (@requires_rdkit), or gemmi (@requires_gemmi) are automatically skipped when those tools are not available.

Contributing

Contributions are welcome. Please open an issue to discuss the change before submitting a pull request.

Fork the repository
Create a feature branch: git checkout -b feature/my-analysis
Make your changes following the conventions in CONTRIBUTING.md
Run the test suite: pytest tests/ -v -m "not integration"
Commit: git commit -m 'Add salt bridge analysis'
Push: git push origin feature/my-analysis
Open a pull request

Please follow existing conventions: NumPy docstrings, type hints, _run() for subprocess calls, and update the relevant sections of this README and CHANGELOG.md.

License

This project is licensed under the MIT License — see LICENSE for details.

Citation

If you use this toolkit in published research, please cite it as:

@software{mdanalysis_gromacs,
  author  = {Sherry, Dean},
  title   = {{MDAnalysis-GROMACS}: An integrated Python toolkit for
             GROMACS simulation preparation and trajectory analysis},
  year    = {2026},
  url     = {https://github.com/DoctorDean/GROMACS-Analysis},
  doi     = {10.5281/zenodo.XXXXXXX},
}

Please also cite the underlying tools your analysis depends on:

Tool	Citation
GROMACS	Abraham et al., SoftwareX (2015). doi:10.1016/j.softx.2015.06.001
AmberTools	Case et al., J. Chem. Inf. Model. (2023). doi:10.1021/acs.jcim.3c01153
ACPYPE	Sousa da Silva & Vranken, BMC Res. Notes (2012). doi:10.1186/1756-0500-5-367
NetworkX	Hagberg et al., Proc. 7th Python in Science Conf. (2008)
Biopython	Cock et al., Bioinformatics (2009). doi:10.1093/bioinformatics/btp163
SciPy	Virtanen et al., Nature Methods (2020). doi:10.1038/s41592-019-0686-2
RDKit	Landrum, G. RDKit: Open-source cheminformatics (2006). rdkit.org
gemmi	Wojdyr, J. Open Source Softw. (2022). doi:10.21105/joss.04200

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
md-configs		md-configs
sim_prep		sim_prep
tests		tests
utils		utils
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
LIGAND_PARAMETERISATION.md		LIGAND_PARAMETERISATION.md
README.md		README.md
STRUCTURE.md		STRUCTURE.md
gitignore		gitignore
gromacs_analysis.py		gromacs_analysis.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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MDAnalysis-GROMACS

Table of Contents

Features

Simulation preparation (sim_prep)

Trajectory analysis (GromacsAnalysis)

Repository Structure

Requirements

External software

Python

Installation

From source (recommended)

Core dependencies only

With structure handling support

For development

Quick Start

Analysis only (existing trajectory)

Preparation from a YAML config file

Simulation Preparation

Configuration Files

Apo Simulations

Protein–Ligand Complex

Mixed-Solvent MixMD

HPC Checkpointing

Trajectory Analysis

Essential Dynamics

Hydrogen Bond Analysis

SASA Analysis

Covariance and DCCM

Correlation Network

Free Energy Landscape

PDB Colouring

Multi-System Comparison

API Reference

SelectionGroups

GromacsAnalysis

sim_prep classes

Output Files

Analysis outputs

Checkpoint file

Visualising Results

PyMOL

ChimeraX

DCCM — interactive inspection

Free energy landscape — custom plotting

Testing

Contributing

License

Citation

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Simulation preparation (`sim_prep`)

Trajectory analysis (`GromacsAnalysis`)

`SelectionGroups`

`GromacsAnalysis`

`sim_prep` classes

Packages