Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis

被引：0
|
作者：
Liu, Yaqiu [1 ]
Chu, Yanshuo [1 ]
Wu, Qu [1 ]
机构：
[1] Northeast Forestry Univ, Coll Informat & Comp Engn, Informat Control & Intelligent Comp Lab, Harbin 150040, Heilongjiang, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY | 2013年 / 48卷 / 01期
关键词：
protein-protein interactions; domain-domain interactions; support vector machine;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more and removing PPIs falsely predicted PPIs involved in glucosinolate biosynthesis in Arabidopsis. A symmetric kernel function is proposed according to the approach of feature representation which combines the domain and domain-domain interaction (DDI) information in this paper. The performance of this kernel indicates SVM based PPIs predictor trained with this kernel is highly effective. According to the prediction result, proteins with Arabidopsis Genome Initiative (AGI) numbers AT4G14800 and AT5G54810, AT5G05730 and AT4G18040, AT1G04510 and AT5G05260 are affirmed as interactive among the 237 low level of confidence PPIs pairs. Furthermore, the SVM-based PPIs predictor is used to explore PPIs of AT1G74090 and AT5G07690 both of which are members of the four glucosinolate biosynthesis pathway proteins absent from AtPIN.
引用
收藏
页码：74 / 82
页数：9
相关论文
共 50 条
  • [11] Predicting protein-protein interactions based on protein-domain relationships
    Wang, B
    Huang, DS
    Chen, P
    Zhu, YP
    Li, YX
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 316 - 319
  • [12] HIME: Mining and Ensembling Heterogeneous Information for Protein-Protein Interactions Prediction
    Chen, Huaming
    Jin, Yaochu
    Wang, Lei
    Chi, Chi-Hung
    Shen, Jun
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [13] Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information
    Li, Yang
    Wang, Zheng
    Li, Li-Ping
    You, Zhu-Hong
    Huang, Wen-Zhun
    Zhan, Xin-Ke
    Wang, Yan-Bin
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [14] Prediction of Protein-Protein Interactions Based on Protein-Protein Correlation Using Least Squares Regression
    Huang, De-Shuang
    Zhang, Lei
    Han, Kyungsook
    Deng, Suping
    Yang, Kai
    Zhang, Hongbo
    CURRENT PROTEIN & PEPTIDE SCIENCE, 2014, 15 (06) : 553 - 560
  • [15] A domain-based approach to predict protein-protein interactions
    Mudita Singhal
    Haluk Resat
    BMC Bioinformatics, 8
  • [16] A domain-based approach to predict protein-protein interactions
    Singhal, Mudita
    Resat, Haluk
    BMC BIOINFORMATICS, 2007, 8 (1)
  • [17] Computational prediction of protein-protein interactions
    Skrabanek, Lucy
    Saini, Harpreet K.
    Bader, Gary D.
    Enright, Anton J.
    MOLECULAR BIOTECHNOLOGY, 2008, 38 (01) : 1 - 17
  • [18] Prediction and redesign of protein-protein interactions
    Lua, Rhonald C.
    Marciano, David C.
    Katsonis, Panagiotis
    Adikesavan, Anbu K.
    Wilkins, Angela D.
    Lichtarge, Olivier
    PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, 2014, 116 (2-3): : 194 - 202
  • [19] Prediction Protein-Protein Interactions with LSTM
    Tao, Zheng
    Yao, Jiahao
    Yuan, Chao
    Zhao, Ning
    Yang, Bin
    Chen, Baitong
    Bao, Wenzheng
    SIMULATION TOOLS AND TECHNIQUES, SIMUTOOLS 2021, 2022, 424 : 540 - 545
  • [20] Prediction of physical protein-protein interactions
    Szilágyi, A
    Grimm, V
    Arakaki, AK
    Skolnick, J
    PHYSICAL BIOLOGY, 2005, 2 (02) : S1 - S16
12345