StoPred is a deep learning-based tool that predicts the stoichiometry of protein complexes from sequence alone, using a combination of protein language model embeddings and a graph attention neural network.

StoPred can handle both homomeric and heteromeric complexes, without requiring template assemblies or predefined stoichiometry. The model outputs the most probable stoichiometry and can suggest a small set of candidates to guide downstream structure prediction.

StoPred achieves state-of-the-art performance compared to Template-based and AlphaFold ranking score-based methods on large-scale benchmark datasets.
Example output