Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network

被引:10
作者
Meng, Zixuan [1 ]
Kuang, Linai [1 ]
Chen, Zhiping [2 ]
Zhang, Zhen [2 ]
Tan, Yihong [2 ]
Li, Xueyong [2 ]
Wang, Lei [1 ,2 ]
机构
[1] Xiangtan Univ, Coll Comp, Xiangtan, Peoples R China
[2] Changsha Univ, Coll Comp Engn & Appl Math, Changsha, Peoples R China
基金
中国国家自然科学基金;
关键词
essential proteins; protein-protein interaction network; computational model; domain-domain interaction network; protein-domain interaction network; DATABASE;
D O I
10.3389/fgene.2021.645932
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Human Protein Structural Interaction Network: Domain Effects on Network Topology and Protein Function
    Chen Li-Na
    Wang Qian
    Shang Yu-Kui
    Zhang Liang-Cai
    Sun Zhao
    He Wei-Ming
    Zhao Yan
    Li Wan
    Wang Hong
    He Yue-Han
    Li Xia
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2010, 37 (05) : 517 - 526
  • [42] UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks
    Peng, Wei
    Wang, Jianxin
    Cheng, Yingjiao
    Lu, Yu
    Wu, Fangxiang
    Pan, Yi
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2015, 12 (02) : 276 - 288
  • [43] Identification of essential proteins based on a new combination of topological and biological features in weighted protein-protein interaction networks
    Elahi, Abdolkarim
    Babamir, Seyed Morteza
    IET SYSTEMS BIOLOGY, 2018, 12 (06) : 247 - 257
  • [44] Refining Protein Interaction Network for Identifying Essential Proteins
    Zhang, Houwang
    Feng, Zhenan
    Wu, Chong
    CURRENT BIOINFORMATICS, 2023, 18 (03) : 255 - 265
  • [45] Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network
    Liu, Hui
    Su, Jianzhong
    Li, Junhua
    Liu, Hongbo
    Lv, Jie
    Li, Boyan
    Qiao, Hong
    Zhang, Yan
    BMC SYSTEMS BIOLOGY, 2011, 5
  • [46] Domain Linker Region Knowledge Contributes to Protein-protein Interaction Prediction
    Zaki, Nazar
    Campbell, Piers
    PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING (IACSIT ICMLC 2009), 2009, : 70 - 74
  • [47] Improving protein-protein interaction prediction using protein language model and protein network features
    Hu, Jun
    Li, Zhe
    Rao, Bing
    Thafar, Maha A.
    Arif, Muhammad
    ANALYTICAL BIOCHEMISTRY, 2024, 693
  • [48] Prediction of Essential Proteins by Integration of PPI Network Topology and Protein Complexes Information
    Ren, Jun
    Wang, Jianxin
    Li, Min
    Wang, Huan
    Liu, Binbin
    BIOINFORMATICS RESEARCH AND APPLICATIONS, 2011, 6674 : 12 - 24
  • [49] Prediction of protein-protein interaction sites using an ensemble method
    Deng, Lei
    Guan, Jihong
    Dong, Qiwen
    Zhou, Shuigeng
    BMC BIOINFORMATICS, 2009, 10
  • [50] Network-based disease gene prioritization based on Protein-Protein Interaction Networks
    Kaushal, Palak
    Singh, Shailendra
    NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 2020, 9 (01):