melodia-py 0.1.8

Protein and RNA/DNA/XNA geometry analysis

Melodia_py

Protein & RNA Structure Analysis

Melodia_py is a Python library for computing Differential Geometry and Knot Theory descriptors of protein and RNA structures.

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What's new in v0.1.8

MDAnalysis integration

• New entry point geometry_from_mdanalysis() — compute per-residue backbone geometry directly from an MDAnalysis Universe, supporting any topology/trajectory format MDAnalysis can read (GROMACS GRO+XTC, AMBER, CHARMM DCD, mmCIF, …). Each trajectory frame is treated as one model, so the output DataFrame has the same schema as geometry_from_structure_file and is immediately compatible with all downstream clustering and alignment functions:

  import MDAnalysis as mda
  import melodia_py as mel
  
  u  = mda.Universe("ubiquitin.gro", "ubiquitin.xtc")
  df = mel.geometry_from_mdanalysis(u, n_jobs=-1)
  # model column = 0-based frame index; same columns as geometry_from_structure_file
  

• Sparse frame sampling — the frames parameter accepts a list of 0-based frame indices, so long trajectories can be analysed at any stride without converting the file first:

  # Every 10th frame of a 1000-frame trajectory
  df = mel.geometry_from_mdanalysis(u, frames=list(range(0, 1000, 10)))
  

• MDAnalysis is an optional dependency — importing melodia_py is unaffected if MDAnalysis is not installed; the ImportError is raised only when geometry_from_mdanalysis() is actually called. Install with:

  pip install "melodia-py[mdanalysis]"
  

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What's new in v0.1.7

Performance

• Parallel model processing — geometry_from_structure_file and geometry_from_structure now accept an n_jobs parameter. For multi-model files (NMR ensembles, MD trajectories) this parallelises across models using all available CPU cores. A 300-model NMR ensemble runs 6.5× faster with n_jobs=8:

  df = mel.geometry_from_structure_file("ensemble.pdb", n_jobs=8)
  

• Numba JIT writhing — the Gauss writhing number double loop is compiled to native code via Numba @njit, giving ~8× speedup on that calculation.
• Adaptive arc-length integration — replaced the hand-rolled Euler loop with scipy.integrate.quad for higher accuracy and fewer function evaluations.

RNA support

• Configurable backbone atom — GeometryParser now accepts an rna_atom parameter (default "C4'") to select the Cα equivalent for RNA chains. C4' is the community standard and is universally present in all nucleotides. Valid choices are exposed as GeometryParser.RNA_ATOMS:

  gp = GeometryParser(chain, rna_atom="C4'")   # default, recommended
  gp = GeometryParser(chain, rna_atom="P")      # phosphorus backbone
  

Correctness fixes

• phi and psi are now correctly typed as Optional[float]. Terminal residues (N-terminus and C-terminus) receive None instead of 0.0.
• RNA detection now uses set membership instead of substring matching, fixing a silent bug for multi-character residue names.
• Division-by-zero guard added in the writhing calculation for degenerate (colinear) atom geometries found in some PDB entries.
• find_gaps is now O(n) instead of O(n²).

Build system

• Migrated from setup.py to pyproject.toml (PEP 517/518). Install with standard pip — no --egg flags needed:

  pip install melodia-py
  pip install "melodia-py[fast,parallel]"   # Numba + joblib extras
  pip install "melodia-py[all]"             # everything
  

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Installation

From PyPI

# To avoid problems with installing NGLView from pip, download the environment.yml file from:
https://github.com/rwmontalvao/Melodia_py.git

# Create and activate the environment
conda env create -f environment.yml
conda activate melodia_py

# Install melodia with pip
pip install melodia-py

With optional extras

Extra	Installs	Enables
fast	numba, llvmlite	JIT-compiled writhing (~8× faster)
parallel	joblib	Multi-core model processing
mdanalysis	MDAnalysis	geometry_from_mdanalysis() for MD trajectories
dev	pytest, mypy, ruff, pre-commit	Development tools
all	all of the above	Everything

pip install "melodia-py[fast,parallel]"

From source (recommended for development)

We recommend Miniforge for environment management.

# Clone the repository
git clone https://github.com/rwmontalvao/Melodia_py.git
cd Melodia_py

# Create and activate the environment
conda env create -f environment.yml
conda activate melodia_py

# Install in editable mode with all extras
pip install -e ".[all]"

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Quick start

import melodia_py as mel

# Single-model structure
df = mel.geometry_from_structure_file("structure.pdb")

# Multi-model NMR ensemble — parallelise across models
df = mel.geometry_from_structure_file("ensemble.pdb", n_jobs=-1)

# RNA structure with C4' backbone atom (default)
df = mel.geometry_from_structure_file("rna.pdb", rna_atom="C4'")

# MD trajectory via MDAnalysis (requires melodia-py[mdanalysis])
import MDAnalysis as mda
u  = mda.Universe("ubiquitin.tpr", "ubiquitin.xtc")
df = mel.geometry_from_mdanalysis(u, n_jobs=-1)          # all frames
df = mel.geometry_from_mdanalysis(u, frames=[0, 50, 100]) # selected frames

# Inspect computed quantities
print(df.columns)
# id, model, code, chain, order, name,
# curvature, torsion, arc_length, writhing, phi, psi

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Documentation

The examples folder contains Jupyter Notebooks with tutorials explaining Melodia_py's functionalities.

Notebook	Open
Getting Started
Alignment Basics
Basic Similarity Analysis
Advanced Similarity Analysis
Machine Learning Ensemble Analysis
Alignment Clustering and PDB Superimposition
RNA Differential Geometry Analysis
Molecular Dynamics Geometry Analysis

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Authors

• Rinaldo W. Montalvão, PhD
• Antonio Marinho da Silva Neto, PhD
• William R. Pitt, PhD

Publication

Melodia: a Python library for protein structure analysis
Bioinformatics, 2024

References

• Montalvão R, Smith R, Lovell S, Blundell T: CHORAL: a differential geometry approach to the prediction of the cores of protein structures. Bioinformatics. 2005, 21: 3719–3725.
• Chang PL, Rinne AW, Dewey TG: Structure alignment based on coding of local geometric measures. BMC Bioinformatics. 2006, 7:346.
• Leung H, Montaño B, Blundell T, Vendruscolo M, Montalvão R: ARABESQUE: A tool for protein structural comparison using differential geometry and knot theory. World Res J Peptide Protein. 2012, 1: 33–40.
• Pitt WR, Montalvão R, Blundell T: Polyphony: superposition independent methods for ensemble-based drug discovery. BMC Bioinformatics. 2014, 15:324.
• Marinho da Silva Neto A, Reghim Silva S, Vendruscolo M, Camilloni C, Montalvão R: A superposition free method for protein conformational ensemble analyses and local clustering based on a differential geometry representation of backbone. Proteins. 2018, 87(4):302–312.
• Marinho da Silva Neto A, Montalvão R, Gondim Martins DB, Lima Filho JL, Madeiros Castelletti CH: A model of key residues interactions for HPVs E1 DNA binding domain–DNA interface based on HPVs residues conservation profiles and molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics. 2019, 38(12):3720–3729.

Works using Melodia_py

Articles

• Arevalo SJ, Felice VVR, Acuña MB, Flores KO, Aguilar MQ, Balan A, Farah CS: The single-particle cryo-EM structures of a bacterial cyanide dihydratase and a fungal cyanide hydratase. Structure. 2026, 34: 599-610.
• Li WR, Cadet XF, Medina-Ortiz D, Davari MD, Sowdhamini R, Damour C, Li Y, Miranville A, Cadet F: From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering. arXiv. 2025: arXiv:2501.02680.

Thesis

• Rasul H: Evaluation of cell permeability in macrocyclic peptides: An exploratory computational chemistry approach. Lund University, 2025.
• Dimitriadis SA: Predicting protein-membrane interfaces of peripheral membrane proteins using machine learning. National and Kapodistian University of Athens, 2026.