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SMENET:一种多视图语义模型,用于多层次的酶功能预测

Hanwen Zhou, Wei Zhang, Zhaohong Deng

IEEE transactions on computational biology and bioinformatics

|December 15, 2025

概括

预测酶功能对于理解生物过程至关重要. 一个新的多视图语义模型SMENET通过整合多种蛋白质序列特征来增强酶功能的预测,克服了传统方法的局限性.

科学领域:

生物化学生物化学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

酶委员会 (EC) 数据将蛋白质序列与催化生物化学反应联系起来,这对于理解生物繁殖和新陈代谢至关重要.
当前的酶功能预测方法面临着诸多挑战,包括复杂的手动特征工程,序列嵌入困难以及处理显著的酶间分布差距.
现有的模型经常提取单视图特征,限制它们捕捉酶数据的复杂性和有效预测多层次函数的能力.

研究的目的:

为了解决现有的酶功能预测方法的局限性.
提出一种新的多层次酶功能预测模型 (SMENET),利用多视图语义.
提高酶功能预测的准确性和全面性.

主要方法:

利用蛋白质大语言模型从酶序列中提取丰富的语义信息.
使用多个信息提取网络模块来处理语义数据.
整合了使用生物语义注意力和多视图自适应融合网络的多种特征视图,以获得最佳的表示提取.

主要成果:

拟议的SMENET模型在跨多个数据集的多层次酶功能预测方面表现出显著的有效性.
多视图语义方法成功地解决了单视图特征提取和分布差距的局限性.
实验验证证了SMENET对现有方法的优越性.

结论:

通过整合多视图语义信息,SMENET提供了一种强大而有效的方法来增强酶功能的预测.
该模型捕获复杂的酶数据表示的能力为更准确的功能注释铺平了道路.
该研究为生物化学和生物信息学研究人员提供了宝贵的工具,代码和数据公开可用.

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