PDBRust: A High-Performance Rust Library for PDB/mmCIF Parsing and Analysis

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1. Verfasser: Fooladi, Hosein
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Veröffentlicht: Zenodo 2026
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author Fooladi, Hosein
author_facet Fooladi, Hosein
contents <p>PDBRust is a fast Rust library for parsing and analyzing Protein Data Bank (PDB) and mmCIF protein structure files. It provides comprehensive support for molecular structure data with robust error handling, structural analysis, quality assessment, and RCSB PDB integration.</p> <p><strong>Key features:</strong></p> <ul> <li>Dual format support: PDB and mmCIF with automatic format detection</li> <li>40-260x faster than equivalent Python implementations</li> <li>Validated against the entire PDB archive (230,655 structures, 100% success rate)</li> <li>Python bindings available via PyPI</li> <li>Feature-gated modules for minimal compile times</li> </ul> <h2>What's New in v0.5.0</h2> <h3>Added</h3> <ul> <li><strong>Distance matrix and contact map calculations</strong> (<code>geometry</code> feature) <ul> <li><code>distance_matrix()</code> for all-atom pairwise distance calculations</li> <li><code>distance_matrix_ca()</code> for CA-only distance matrix</li> <li><code>contact_map(threshold)</code> for all-atom contact detection</li> <li><code>contact_map_ca(threshold)</code> for CA contact maps</li> <li>Returns efficient numpy arrays in Python bindings</li> </ul> </li> <li><strong>RMSD calculation and structure superposition</strong> (<code>geometry</code> feature) <ul> <li><code>rmsd_to()</code> for RMSD calculation between structures</li> <li><code>align_to()</code> for structure superposition using Kabsch algorithm</li> <li><code>per_residue_rmsd_to()</code> for per-residue RMSD analysis</li> <li><code>AlignmentResult</code> with RMSD, rotation matrix, and translation vector</li> <li><code>PerResidueRmsd</code> for flexibility analysis</li> </ul> </li> <li><strong>Python bindings for geometry features</strong> <ul> <li>Full numpy integration for distance matrices and contact maps</li> <li>Structure alignment returns (aligned_structure, AlignmentResult) tuple</li> </ul> </li> </ul> <h3>Changed</h3> <ul> <li><strong>Platform-specific Python builds</strong> <ul> <li>Linux wheels built with <code>core</code> features (without RCSB) to avoid OpenSSL cross-compilation issues</li> <li>macOS and Windows wheels built with <code>full</code> features (including RCSB)</li> <li>All other features (parsing, filtering, descriptors, geometry, numpy) work on all platforms</li> </ul> </li> </ul> <h3>Fixed</h3> <ul> <li>Linux wheel builds now work reliably without OpenSSL dependency issues</li> </ul> <h2>Installation</h2> <p><strong>Python:</strong></p> <pre><code>pip install pdbrust </code></pre> <p><strong>Rust:</strong></p> <pre><code>[dependencies] pdbrust = "0.5" </code></pre> <h2>Full Changelog</h2> <p>See <a href="https://github.com/HFooladi/pdbrust/blob/main/CHANGELOG.md">CHANGELOG.md</a></p>
format Recurso digital
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institution Zenodo
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publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle PDBRust: A High-Performance Rust Library for PDB/mmCIF Parsing and Analysis
Fooladi, Hosein
bioinformatics
protein structure
PDB
mmCIF
structural biology
Rust
Python
molecular structure
protein analysis
computational biology
<p>PDBRust is a fast Rust library for parsing and analyzing Protein Data Bank (PDB) and mmCIF protein structure files. It provides comprehensive support for molecular structure data with robust error handling, structural analysis, quality assessment, and RCSB PDB integration.</p> <p><strong>Key features:</strong></p> <ul> <li>Dual format support: PDB and mmCIF with automatic format detection</li> <li>40-260x faster than equivalent Python implementations</li> <li>Validated against the entire PDB archive (230,655 structures, 100% success rate)</li> <li>Python bindings available via PyPI</li> <li>Feature-gated modules for minimal compile times</li> </ul> <h2>What's New in v0.5.0</h2> <h3>Added</h3> <ul> <li><strong>Distance matrix and contact map calculations</strong> (<code>geometry</code> feature) <ul> <li><code>distance_matrix()</code> for all-atom pairwise distance calculations</li> <li><code>distance_matrix_ca()</code> for CA-only distance matrix</li> <li><code>contact_map(threshold)</code> for all-atom contact detection</li> <li><code>contact_map_ca(threshold)</code> for CA contact maps</li> <li>Returns efficient numpy arrays in Python bindings</li> </ul> </li> <li><strong>RMSD calculation and structure superposition</strong> (<code>geometry</code> feature) <ul> <li><code>rmsd_to()</code> for RMSD calculation between structures</li> <li><code>align_to()</code> for structure superposition using Kabsch algorithm</li> <li><code>per_residue_rmsd_to()</code> for per-residue RMSD analysis</li> <li><code>AlignmentResult</code> with RMSD, rotation matrix, and translation vector</li> <li><code>PerResidueRmsd</code> for flexibility analysis</li> </ul> </li> <li><strong>Python bindings for geometry features</strong> <ul> <li>Full numpy integration for distance matrices and contact maps</li> <li>Structure alignment returns (aligned_structure, AlignmentResult) tuple</li> </ul> </li> </ul> <h3>Changed</h3> <ul> <li><strong>Platform-specific Python builds</strong> <ul> <li>Linux wheels built with <code>core</code> features (without RCSB) to avoid OpenSSL cross-compilation issues</li> <li>macOS and Windows wheels built with <code>full</code> features (including RCSB)</li> <li>All other features (parsing, filtering, descriptors, geometry, numpy) work on all platforms</li> </ul> </li> </ul> <h3>Fixed</h3> <ul> <li>Linux wheel builds now work reliably without OpenSSL dependency issues</li> </ul> <h2>Installation</h2> <p><strong>Python:</strong></p> <pre><code>pip install pdbrust </code></pre> <p><strong>Rust:</strong></p> <pre><code>[dependencies] pdbrust = "0.5" </code></pre> <h2>Full Changelog</h2> <p>See <a href="https://github.com/HFooladi/pdbrust/blob/main/CHANGELOG.md">CHANGELOG.md</a></p>
title PDBRust: A High-Performance Rust Library for PDB/mmCIF Parsing and Analysis
topic bioinformatics
protein structure
PDB
mmCIF
structural biology
Rust
Python
molecular structure
protein analysis
computational biology
url https://doi.org/10.5281/zenodo.18232204