ProtSpace: A Tool for Visualizing Protein Space
Tobias Senoner, Tobias Olenyi, Michael Heinzinger, Anton Spannagl, George Bouras, Burkhard Rost, Ivan Koludarov
Abstract
• Protein embeddings are powerful but impossible to interpret directly. • ProtSpace enables interactive visualization of protein embedding spaces. • Merging 3D protein structure and embedding visuals helps generate new hypotheses. • Enables quick analysis and sharing of labeled embedding spaces for large protein sets. • ProtSpace democratizes pLMs by making embedding visualization accessible. Protein language models (pLMs) generate high-dimensional representations of proteins, so called embeddings, that capture complex information stored in the set of evolved sequences. Interpreting these embeddings remains an important challenge. ProtSpace provides one solution through an open-source Python package that visualizes protein embeddings interactively in 2D and 3D. The combination of embedding space with protein 3D structure view aids in discovering functional patterns readily missed by traditional sequence analysis. We present two examples to showcase ProtSpace . First, investigations of phage data sets showed distinct clusters of major functional groups and a mixed region, possibly suggesting bias in today’s protein sequences used to train pLMs. Second, the analysis of venom proteins revealed unexpected convergent evolution between scorpion and snake toxins; this challenges existing toxin family classifications and added evidence refuting the aculeatoxin family hypothesis . ProtSpace is freely available as a pip-installable Python package (source code & documentation) with examples on GitHub (https://github.com/tsenoner/protspace) and as a web interface (https://protspace.rostlab.org). The platform enables seamless collaboration through portable JSON session files.