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A visualisation tool for protein embeddings from pLMs

Project description

ProtSpace

PyPI version Python 3.10+ License: GPL v3 Downloads DOI

ProtSpace is a visualization tool for exploring protein embeddings or similarity matrices. It projects high-dimensional protein language model data into 2D space, color-codes proteins by biological annotations, and exports publication-ready figures.

  • Multiple projections: PCA, UMAP, t-SNE, MDS, PaCMAP
  • Automatic annotations: UniProt, InterPro, and Taxonomy
  • Structure viewer: Integrated protein structure visualization
  • Export: PNG, PDF, SVG, HTML

🌐 Try Online

ProtSpace Web (recommended): Fast 2D explorer optimized for large datasets — drag & drop .parquetbundle files (source)

Legacy Dash frontend: Original interface with 3D support (slow with large datasets)

🚀 Google Colab Notebooks

Note: Use Chrome or Firefox for best experience.

  1. Generate Protein Embeddings: Open Embeddings In Colab

  2. Prepare ProtSpace Bundle: Open Preparation In Colab

  3. Full Pipeline Demo: Open Pipeline In Colab

📦 Installation

pip install protspace

🎯 Quick Start

1. Process local data

protspace-local -i embeddings.h5 -o output_dir -m pca2,umap2

2. Explore results

Upload the generated .parquetbundle file at protspace.app/explore.

📊 Example Output

2D Example

✨ Annotations

Use -a to color-code proteins by UniProt, InterPro, or Taxonomy annotations. Groups (default, all, uniprot, interpro, taxonomy) and individual names can be mixed freely. If -a is omitted, the default group is used.

protspace-local -i data.h5                              # default UniProt annotations (fast)
protspace-local -i data.h5 -a default,interpro,kingdom  # mix groups and individual names

📖 Documentation

📝 Citation

Senoner T, Olenyi T, Heinzinger M, Spannagl A, Bouras G, Rost B, Koludarov I. ProtSpace: A Tool for Visualizing Protein Space. Journal of Molecular Biology, 168940, 2025. doi:10.1016/j.jmb.2025.168940

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