Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912539024031744 |
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| author | Wong, Brian Shing-Hei Kim, Joshua Mincheol Fung, Sin-Hang Xiong, Qing Ao, Kelvin Fu-Kiu Wei, Junkang Wang, Ran Wang, Dan Michelle Zhou, Jingying Feng, Bo Cheng, Alfred Sze-Lok Yip, Kevin Y. Tsui, Stephen Kwok-Wing Cao, Qin |
| author_facet | Wong, Brian Shing-Hei Kim, Joshua Mincheol Fung, Sin-Hang Xiong, Qing Ao, Kelvin Fu-Kiu Wei, Junkang Wang, Ran Wang, Dan Michelle Zhou, Jingying Feng, Bo Cheng, Alfred Sze-Lok Yip, Kevin Y. Tsui, Stephen Kwok-Wing Cao, Qin |
| contents | Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10541 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation Wong, Brian Shing-Hei Kim, Joshua Mincheol Fung, Sin-Hang Xiong, Qing Ao, Kelvin Fu-Kiu Wei, Junkang Wang, Ran Wang, Dan Michelle Zhou, Jingying Feng, Bo Cheng, Alfred Sze-Lok Yip, Kevin Y. Tsui, Stephen Kwok-Wing Cao, Qin Machine Learning Quantitative Methods Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research. |
| title | Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2508.10541 |