Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909497709035520 |
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| author | Zhang, Jiayang Liu, Xianyuan Wu, Wei Tabakhi, Sina Fan, Wenrui Zhou, Shuo Tee, Kang Lan Wong, Tuck Seng Lu, Haiping |
| author_facet | Zhang, Jiayang Liu, Xianyuan Wu, Wei Tabakhi, Sina Fan, Wenrui Zhou, Shuo Tee, Kang Lan Wong, Tuck Seng Lu, Haiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning Zhang, Jiayang Liu, Xianyuan Wu, Wei Tabakhi, Sina Fan, Wenrui Zhou, Shuo Tee, Kang Lan Wong, Tuck Seng Lu, Haiping Machine Learning Biomolecules Quantitative Methods 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. |
| title | Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning |
| topic | Machine Learning Biomolecules Quantitative Methods |
| url | https://arxiv.org/abs/2502.12049 |