Explore protein language model embeddings in your browser — surface relationships sequence similarity misses, overlay annotations, and transfer labels (EAT). Nothing uploaded.
See the code
ProtSpace is a free, in-browser tool for exploring protein language model (pLM) embeddings. Project embeddings into an interactive map to surface relationships that sequence similarity misses, overlay biological annotations, transfer labels to unannotated proteins with a confidence score (EAT), inspect 3D structures, and export publication-ready figures, at Swiss-Prot scale, with your bundle never leaving the browser.
protspace.app, drag & drop a .parquetbundle file to start exploring. Loading, exploring and exporting a bundle run entirely client-side, the file never leaves your machine. You can also drop a .fasta for instant preparation on supported deployments; that path uploads your sequences to the ProtSpace prep backend, which computes the embeddings a browser cannot. See Is my data uploaded to a server? for details.
Option 1: Google Colab (no local installation)
Generate .parquetbundle files directly in your browser:
Option 2: ProtSpace Python package (local installation)
pip install protspace
# Build a visualization bundle from your embeddings (HDF5)
protspace prepare -i embeddings.h5 -m pca2,umap2 -o output
# ...or from a FASTA file (auto-embeds via the Biocentral API)
protspace prepare -i sequences.fasta -e prot_t5 -m pca2 -o output
See the ProtSpace Python package for the full CLI (annotation transfer, quality metrics, multiple pLMs, and more).
Full documentation, user guides, data preparation, and feature explanations.
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. (2026). ProtSpace: Protein Universe in Your Browser. bioRxiv. https://doi.org/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. (2025). ProtSpace: A Tool for Visualizing Protein Space. Journal of Molecular Biology, 437(15), 168940. https://doi.org/10.1016/j.jmb.2025.168940
A machine-readable CITATION.cff is included, use GitHub's "Cite this repository" button to export BibTeX or APA.
Explore protein language model embeddings in your browser — surface relationships sequence similarity misses, overlay annotations, and transfer labels (EAT). Nothing uploaded.
See the code
ProtSpace is a free, in-browser tool for exploring protein language model (pLM) embeddings. Project embeddings into an interactive map to surface relationships that sequence similarity misses, overlay biological annotations, transfer labels to unannotated proteins with a confidence score (EAT), inspect 3D structures, and export publication-ready figures, at Swiss-Prot scale, with your bundle never leaving the browser.
protspace.app, drag & drop a .parquetbundle file to start exploring. Loading, exploring and exporting a bundle run entirely client-side, the file never leaves your machine. You can also drop a .fasta for instant preparation on supported deployments; that path uploads your sequences to the ProtSpace prep backend, which computes the embeddings a browser cannot. See Is my data uploaded to a server? for details.
Option 1: Google Colab (no local installation)
Generate .parquetbundle files directly in your browser:
Option 2: ProtSpace Python package (local installation)
pip install protspace
# Build a visualization bundle from your embeddings (HDF5)
protspace prepare -i embeddings.h5 -m pca2,umap2 -o output
# ...or from a FASTA file (auto-embeds via the Biocentral API)
protspace prepare -i sequences.fasta -e prot_t5 -m pca2 -o output
See the ProtSpace Python package for the full CLI (annotation transfer, quality metrics, multiple pLMs, and more).
Full documentation, user guides, data preparation, and feature explanations.
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. (2026). ProtSpace: Protein Universe in Your Browser. bioRxiv. https://doi.org/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. (2025). ProtSpace: A Tool for Visualizing Protein Space. Journal of Molecular Biology, 437(15), 168940. https://doi.org/10.1016/j.jmb.2025.168940
A machine-readable CITATION.cff is included, use GitHub's "Cite this repository" button to export BibTeX or APA.