Yu Lezheng, Luo Yuxin, Wu Shiqi, Chen Siyi, Xue Li, Jing Runyu, Luo Jiesi
School of Mathematics and Big Data, Guizhou Education University, Guiyang, 550018, China.
School of Chemistry and Materials Science, Guizhou Education University, Guiyang , Guizhou, 550018, China.
BMC Biol. 2025 Jul 31;23(1):232. doi: 10.1186/s12915-025-02347-z.
Accurate prediction of allergens is essential for identifying the sources of allergic reactions and preventing future exposure to harmful triggers; however, the limited performance of current prediction tools hinders their practical applications.
Here, we present Multimodal-AlgPro, a unified framework based on a multimodal deep learning algorithm designed to predict allergens by integrating multiple dimensions, including physicochemical properties, amino acid sequences, and evolutionary information. An exhaustive search strategy for model combinations has also been introduced to ensure robust allergen prediction by thoroughly exploring every possible modality configuration to determine the most effective framework architecture. Additionally, identifying explainable sequence motifs and molecular descriptors from these models that facilitate epitope discovery is of interest. Because it leverages diverse heterogeneous features and our improved multimodal data fusion, Multimodal-AlgPro outperformed several existing methods, demonstrating its potential to significantly advance the accuracy of allergen prediction.
Overall, Multimodal-AlgPro is a valuable tool for deciphering the mechanisms of allergic responses and offers novel insights on epitope design, with applications in both public health and industrial sectors.
准确预测过敏原对于识别过敏反应的来源以及预防未来接触有害触发因素至关重要；然而，当前预测工具的有限性能阻碍了它们的实际应用。
在此，我们展示了Multimodal-AlgPro，这是一个基于多模态深度学习算法的统一框架，旨在通过整合多个维度来预测过敏原，这些维度包括物理化学性质、氨基酸序列和进化信息。还引入了一种用于模型组合的穷举搜索策略，通过全面探索每种可能的模态配置来确定最有效的框架架构，以确保可靠的过敏原预测。此外，从这些模型中识别有助于表位发现的可解释序列基序和分子描述符也很有意义。由于Multimodal-AlgPro利用了多样的异构特征以及我们改进的多模态数据融合，它优于几种现有方法，证明了其显著提高过敏原预测准确性的潜力。
总体而言，Multimodal-AlgPro是用于解读过敏反应机制的宝贵工具，并为表位设计提供了新的见解，在公共卫生和工业领域均有应用。
