Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to form a VLP, is critical for vaccine optimisation. However, current experimental methods to determine stoichiometry are time-consuming and require highly purified proteins. To efficiently classify stoichiometry classes in proteins, we curate a new dataset and propose an interpretable, data-driven pipeline leveraging linear machine learning models. We also explore the impact of feature encoding on model performance and interpretability, as well as methods to identify key protein sequence features influencing classification. The evaluation of our pipeline demonstrates that it can classify stoichiometry while revealing protein features that possibly influence VLP assembly. The data and code used in this work are publicly available at https://github.com/Shef-AIRE/StoicIML.
@article{arxiv.2502.12049,
title = {Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning},
author = {Jiayang Zhang and Xianyuan Liu and Wei Wu and Sina Tabakhi and Wenrui Fan and Shuo Zhou and Kang Lan Tee and Tuck Seng Wong and Haiping Lu},
journal= {arXiv preprint arXiv:2502.12049},
year = {2025}
}