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通过超图学习整合蛋白序列和蛋白质与蛋白质相互作用数据,以识别新型蛋白质复合体

Simin Xia^1,2, Dianke Li^2,3, Xinru Deng²

¹School of Basic Medical Sciences, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei 230032, China.

Briefings in bioinformatics

|June 8, 2024

概括

我们开发了HyperGraphComplex,这是一种新的计算方法,通过整合蛋白序列和蛋白质-蛋白质相互作用 (PPI) 网络拓来预测蛋白质复合体. 这种方法提高了对生物过程的理解,有助于疾病治疗和药物开发.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
系统生物学系统生物学

背景情况:

蛋白与蛋白相互作用 (PPI) 通过蛋白质复合体调解关键的生物功能.
蛋白质复合体的实验识别是有限的,需要计算方法.
现有的计算方法往往无法有效地整合蛋白序列和PPI网络拓.

研究的目的:

开发一个有效的计算框架来预测蛋白质复合体.
为了利用蛋白质序列特征和PPI网络拓来提高预测准确性.
为了解细胞过程和促进药物开发提供有价值的资源.

主要方法:

开发了HyperGraphComplex,一种使用超图变量自编码器的方法.
从蛋白质序列中集成表达特征,而无需手动特征工程.
包含来自蛋白质-蛋白质相互作用网络的拓性质.

主要成果:

与最先进的方法相比,HyperGraphComplex表现出令人满意的预测性能.
预测的蛋白质复合体表现出与已知的复合体相似的属性.
案例研究验证了模型的能力,识别了由最近的实验和结构预测 (AlphaFold-Multimer) 支持的复合体.

结论:

HyperGraphComplex通过整合多种数据源,为蛋白质复合体预测提供了一种有前途的方法.
开发的算法和数据集可以促进对细胞调节中的蛋白质复杂功能的理解.
这项工作促进了疾病诊断,治疗和药物开发的进步.

关键词:

超图形学习的学习方法蛋白质复合体是一种蛋白质复合体.蛋白质网络拓学蛋白质序列的蛋白质序列是什么

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