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Updated: Aug 25, 2026
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Stoic: fast and accurate protein stoichiometry prediction
Daniil Litvinov1,2, Lorenzo Pantolini1,2, Peter Škrinjar1,2
1Biozentrum, University of Basel, Basel 4056, Switzerland.
Motivation:
Protein complexes are central to cellular function, but experimental determination of their structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry-the number of copies of each protein entity within a complex. Current approaches rely on computationally expensive brute-force methods that run structure prediction on multiple stoichiometry combinations, often with limited accuracy.
Results:
We introduce Stoic, a method that uses protein language model embeddings to predict protein complex stoichiometry. Our approach learns to identify interface residues that participate in protein-protein interactions, rather than relying on global sequence features. By integrating these interface-aware embeddings into a graph neural network, Stoic achieves fast and accurate stoichiometry prediction for both homomeric and heteromeric targets.
Availability:
Source code for inference and training along with web versions are available in the repository at https://github.com/PickyBinders/stoic.
