ProtSpace

PyPI version Python 3.10+ License: MIT Downloads DOI (preprint) DOI (JMB)

ProtSpace maps the embedding space of protein language models (pLMs) to reveal relationships that sequence similarity misses. This Python package prepares your data — embed sequences, project to 2D, overlay biological annotations (UniProt, InterPro, AlphaFold/TED, ML predictions), and transfer labels from the nearest neighbour in embedding space (EAT) — then bundles everything into a .parquetbundle you explore interactively at protspace.app, nothing uploaded. Similarity matrices are supported as input too.

  • Multiple projections: linear and non-linear dimensionality reduction (PCA, UMAP, t-SNE, and more)
  • Automatic annotations: UniProt, InterPro, Taxonomy, TED domains, and Biocentral predictions
  • Quality metrics (opt-in): annotation-based cluster-validity + faithfulness (local & global) via --stats
  • Annotation transfer (EAT): fill missing annotations from the nearest reference proteins in embedding space via protspace transfer
  • Structure viewer: Integrated protein structure visualization
  • Export: PNG, PDF, SVG, HTML

🌐 Try Online

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

🚀 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. Transfer Annotations (EAT): Open Transfer In Colab

📦 Installation

pip install protspace

🎯 Quick Start

1. Prepare data

# From HDF5 embeddings
protspace prepare -i embeddings.h5 -m pca2,umap2 -o output

# From FASTA (auto-embeds via Biocentral API)
protspace prepare -i sequences.fasta -e prot_t5 -m pca2 -o output

# Multi-model comparison (compare across pLMs)
protspace prepare -i sequences.fasta -e prot_t5,esm2_650m,ankh_base -m pca2,umap2 -o output

# Combine datasets (same embedding name → proteins are unioned)
protspace prepare -i species_a.h5:prot_t5 -i species_b.h5:prot_t5 -m umap2 -o output

2. Explore results

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

3. Power-user workflow (individual steps)

protspace embed -i sequences.fasta -e prot_t5 -e esm2_3b -o embeddings/
protspace project -i embeddings/prot_t5.h5 -i embeddings/esm2_3b.h5 -m pca2,umap2 -o projections/
protspace annotate -i embeddings/prot_t5.h5 -a default -o annotations.parquet
protspace stats -i embeddings/prot_t5.h5 -p projections/ -o statistics.parquet   # optional: quality metrics
protspace bundle -p projections/ -a annotations.parquet -s statistics.parquet -o output.parquetbundle
protspace transfer -b output.parquetbundle -e embeddings/prot_t5.h5 -t superfamily -o transferred.parquetbundle   # optional: fill gaps via EAT

Or compute quality metrics inline during prepare with --stats (opt-in): annotation-based cluster-validity + faithfulness per projection. See the CLI Reference.

Fill missing annotation values from the nearest annotated protein in embedding space with protspace transfer — Embedding Annotation Transfer (EAT).

📊 Example Output

2D Example

✨ Annotations

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

protspace prepare -i data.h5 -m pca2                              # default annotations
protspace prepare -i data.h5 -a default,interpro,kingdom -m pca2  # mix groups + individual

📖 Documentation

📝 Citation

If you use ProtSpace, please cite the web application preprint (latest):

Senoner T, Vahidi P, Olenyi T, Senoner F, Sisman G, Kahl E, Rost B, Koludarov I. ProtSpace: Protein Universe in Your Browser. bioRxiv, 2026. doi:10.64898/2026.05.04.722720

The original, peer-reviewed ProtSpace publication:

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, 437(15), 168940, 2025. doi:10.1016/j.jmb.2025.168940

Project links

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  • Released:
Newer release available (4.12.0)

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Avatar for tsenoner from gravatar.com tsenoner

Credits

Author: Tobias Senoner

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MIT
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Requires

Python >=3.12

Provides Extra

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