Stoic: Fast and accurate protein stoichiometry prediction
Journal:
bioRxiv
Published Date:
Mar 16, 2026
Abstract
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.
