A new open source toolkit called ProT-Vision has just been released, enabling fast and interpretable classification of protein structures using AI. Designed by a team from EMBL and ETH Zurich, ProT-Vision leverages visual representation learning to identify structural patterns in protein folds, active sites, and domains.
What Makes It Different
- Converts 3D protein data into image-like grids for CNN analysis
- Supports PDB and AlphaFold formats with automatic preprocessing
- Pretrained models for SCOP and CATH classification
- Interactive notebooks and plugins for PyMOL and ChimeraX
Example Code
from protvision.io import load_structure
from protvision.model import FoldClassifier
protein = load_structure("1CRN.pdb")
classifier = FoldClassifier(pretrained=True)
label = classifier.predict(protein)
print("Predicted fold:", label)
Real-World Impact
ProT-Vision enables protein structure researchers to annotate large datasets in seconds instead of hours. Its accuracy rivals traditional structural alignment tools, while being far more scalable. Applications include drug target classification, enzyme function prediction, and evolutionary analysis.
By using CNNs on voxelized structures, the tool avoids overfitting and provides saliency maps that highlight functionally relevant regions in the protein.
Availability
The toolkit is hosted on GitHub with detailed docs, Docker containers, and ready-to-use datasets. It is compatible with Linux, Windows, and macOS and requires only PyTorch and Biopython to get started.
Sources
https://github.com/protvision-ai/protvision
https://www.embl.org/news/science/protein-classification-ai-release-2025/
https://academic.oup.com/bioinformatics/article/41/6/btad212/7698231
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