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KSMoFinder - 嵌入蛋白质和基因的知识图,用于预测人类酸盐的激酶

Manju Anandakrishnan¹, Karen E Ross², Chuming Chen¹

¹Center for Bioinformatics and Computational Biology, University of Delaware, Newark, United States of America.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

KSMoFinder通过将蛋白质生物背景与序列动图集成来预测酶-基质关系. 这种新的方法超越了现有的工具,在识别酶酸化位点方面提供了更高的准确性.

科学领域:

分子生物学分子生物学
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:

蛋白激酶是细胞信号通路的关键调节者,通过酸化特定基质蛋白质来调解它们的功能.
目前的预测工具主要依赖于酸化部位周围的氨基酸序列动机,往往忽视了基底蛋白质的更广泛的生物背景.

研究的目的:

开发KSMoFinder,一种新的酶-基质-动机预测模型,该模型包含了蛋白质的生物背景,超出了序列动机.
为了提高酶-酸关系预测的准确性和范围.

主要方法:

KSMoFinder从知识图中整合了蛋白质语义,包括上下文关系,酶特异性动机和动机组合.
蛋白质和图案被表示为嵌入式载体,作为监督深度学习分类器的特征.
来自iPTMnet和PhosphositePlus的基础真相数据被用于培训和评估.

主要成果:

与现有的酶基质预测工具相比,KSMoFinder表现出优异的性能.
该模型在平衡的测试数据集上实现了0.851的ROC-AUC和0.839的PR-AUC,超过了流行的蛋白质语言模型嵌入.
KSMoFinder可以预测动机和基质蛋白质水平,提供更大的多功能性.

结论:

整合生物背景显著提高了酶-基质-基因预测的准确性.
在预测酶-酸盐关系方面,KSMoFinder代表了实质性的进步.
该模型在多个层面上进行预测的能力为生物研究提供了有价值的工具.

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