计算机科学
基因本体论
蛋白质-蛋白质相互作用
一般化
水准点(测量)
蛋白质测序
计算模型
机器学习
计算生物学
人工智能
药物发现
数据挖掘
生物信息学
肽序列
基因
生物
数学
数学分析
基因表达
生物化学
遗传学
地理
大地测量学
作者
Shaojun Xie,Xiaojun Xie,Xiaoyan Zhao,Fei Liu,Yiming Wang,Jihui Ping,Zhiwei Ji
摘要
Abstract Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein–Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.
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