Skip to content

Protein Data Crystallization Extraction V1.0.1

Latest
Latest

Choose a tag to compare

View all tags
@Njantang1 Njantang1 released this 09 Jun 15:47
v1.0.1
2c4f17f

🎉 Protein Crystallization Data Extraction (PCDE) V1.0.1

Protein Crystallization Data Extraction Tool
by Nana Njantang Ruth · ORCID 0000-0002-6003-7521

Overview

Protein crystallography remains one of the primary methods for determining three-dimensional protein structures. Identifying the right crystallization conditions is a critical bottleneck. This pipeline mines the RCSB Protein Data Bank (PDB) to retrieve, filter, annotate, and visualize crystallization condition data for any input sequence — turning raw PDB metadata into structured, FAIR-compliant datasets.

What's included in this release

🗂️ Input & FASTA Export

  • Accepts any amino acid, DNA, or RNA sequence interactively
  • Automatically detects sequence type (protein / DNA / RNA)
  • Saves the input sequence as a FASTA file to output/{seq_type_name}/

🔍 Step 1 — RCSB Sequence Search (rcsb_sequence_identity.py)

  • Queries the RCSB Search API (/rcsbsearch/v2/query) using sequence similarity scoring
  • Maps sequence type to the correct target (pdb_protein_sequence, pdb_dna_sequence, pdb_rna_sequence)
  • Filters results to X-ray crystallography only via a per-hit lookup to /rest/v1/core/entry/{pdb_id}
  • Extracts per-hit: PDB_ID, Entity, Score, Seq_id (sequence identity), E-value
  • Saves ranked hits to {seq_type_name}_rcsb_hits.csv

🧹 Step 2 — Crystallization Data Retrieval & Filtering (PDB_searchAPI.py)

  • Runs a full PDB search for crystallization metadata
  • Filters entries by experimental conditions via filter_experimental_conditions()
  • All intermediate temporary CSV files are automatically cleaned up after each stage

🧪 Compound Annotation (extract_structures.py)

  • Enriches filtered crystallization data with a COMPOUND column using structures.pkl
  • Output stored in a temporary CSV before merging

🔗 Step 3 — Merge RCSB + Crystallization Data

  • Merges RCSB sequence-identity results with crystallization condition data on PDB_ID
  • Final merged CSV contains a defined column order: 
    PDB_ID · Entity · Score · Seq_id · E-value · Resolution · Pubmed_id · Method · pH · Temp · Ligands · Polymer · Assembly · pdbx_pH_range · pdbx_details · Compounds (mM) · PEG_Id · PEG_con
  • Saved as {seq_type_name}_merged_results.csv

📊 Step 4 — High-Resolution Visualization (plot.py)

  • run_plot() generates 300 dpi analytical scatter plots from the merged CSV:
    • pH vs. Temperature (K)
    • pH vs. PEG concentration (%)

📄 Consolidated PDF Report

  • Outputs Cryst_cocktail_Table.pdf: a colored summary table compiling PDB IDs, alignment metrics, ligands, and complete chemical cocktails into a single laboratory resource

Pipeline Flow

Sequence input
     │
     ├──► FASTA export
     │
     ├──► Step 1: RCSB sequence search + X-ray filter → rcsb_hits.csv
     │
     ├──► Step 2: PDB crystallization search (×6 workers) → temp CSV
     │                └── filter experimental conditions → temp CSV
     │                        └── append COMPOUND column → temp CSV
     │
     ├──► Step 3: Merge RCSB + crystallization data → merged_results.csv
     │
     └──► Step 4: run_plot() → scatter plots (300 dpi)

Output Structure

output/
└── {seq_type_name}/
    ├── {seq_type_name}_sequence.fasta
    ├── {seq_type_name}_rcsb_hits.csv
    ├── {seq_type_name}_merged_results.csv
    ├── Cryst_cocktail_Table.pdf
    └── plots/
        ├── pH_vs_Temperature.png
        └── pH_vs_PEG_concentration.png

Module Overview

Module Role
main.py Pipeline orchestration and I/O
rcsb_sequence_identity.py RCSB sequence search + X-ray filter
PDB_searchAPI.py Crystallization metadata retrieval & filtering
extract_structures.py Compound annotation via structures.pkl
plot.py High-resolution scatter plot generation

Dependencies

  • Python 3.8+
  • requests, pandas, openpyxl, matplotlib, concurrent.futures, tempfile

Install all dependencies with:

pip install -r requirements.txt

📦 Download Assets

The following pre-built archives are available for this release:

Asset Format Size Description
Source code (zip) ZIP - Complete repository as ZIP archive
Source code (tar.gz) TAR.GZ - Complete repository as compressed TAR archive

Direct download links:

How to Install

Option 1: From ZIP Archive

# Download and extract
unzip V1.0.0.zip
cd Protein_Crystallization_Data_Extraction

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Option 2: Clone from Git

git clone -b V1.0.0 https://github.com/RitAreaSciencePark/Protein_Crystallization_Data_Extraction.git
cd Protein_Crystallization_Data_Extraction
pip install -r requirements.txt

Quick Start

Web Application

cd protein_crystallization_app
python manage.py migrate
python manage.py runserver

Then visit http://127.0.0.1:8000

Command-Line (Single Sequence)

cd src
python Main.py
Enter sequence: MSPRKTYILKLYVAGNTPNSVRALK...
Enter a descriptive sequence type name: MyProtein

Command-Line (FASTA Batch)

cd src_fasta_file
python main.py input.fasta

How to Cite

If you use this pipeline in your research, please cite:

Nana Njantang Ruth, Valerio PIOMPONI, and Adrea DALLE VEDOVE (2026). Protein Crystallization Data Extraction Tool (v1.0.0). GitHub Repository. https://github.com/RitAreaSciencePark/Protein_Crystallization_Data_Extraction

Or use the CITATION.cff file included in this repository.

Known Limitations

  • Compound annotation depends on the static structures.pkl reference file
  • Free-text crystallization fields in PDB are not fully standardized across all entries
  • Web interface improvements planned for v2.0.0

License

MIT © 2025 RitAreaSciencePark

This project is released under the MIT License. See the LICENSE file for details.

Support & Feedback

For issues, feature requests, or questions:

Thank you for using PCDE! 🧬

Full Changelog: V1.0.0...v1.0.1

Assets 2
Loading
0 Join discussion