A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology

被引:134
|
作者
Chou, KC [1 ]
Cai, YD
机构
[1] Gordon Life Sci Inst, San Diego, CA 92130 USA
[2] TIBDD, Tianjin, Peoples R China
[3] UMIST, Biomol Sci Dept, Manchester M60 1QD, Lancs, England
关键词
gene ontology; functional domain composition; pseudo-amino acid composition; InterPro database; hybrid space; intimate sorting algorithm; ISort predictor; protein subcellular location;
D O I
10.1016/j.bbrc.2003.10.062
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1) the biological objective to which the gene or gene product contributes, (2) the biochemical activity of a gene product, and (3) the place in the cell where a gene product is active. (C) 2003 Elsevier Inc. All rights reserved.
引用
收藏
页码:743 / 747
页数:5
相关论文
共 50 条
  • [1] A new hybrid approach to predict subcellular localization by incorporating protein evolutionary conservation information
    Zhang, ShaoWu
    Zhang, YunLong
    Li, JunHui
    Yang, HuiFeng
    Cheng, YongMei
    Zhou, GuoPing
    [J]. LIFE SYSTEM MODELING AND SIMULATION, PROCEEDINGS, 2007, 4689 : 172 - +
  • [2] HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins
    Wan, Shibiao
    Mak, Man-Wai
    Kung, Sun-Yuan
    [J]. PLOS ONE, 2014, 9 (03):
  • [3] Subcellular localization prediction for human internal and organelle membrane proteins with projected gene ontology scores
    Du, Pufeng
    Tian, Yang
    Yan, Yan
    [J]. JOURNAL OF THEORETICAL BIOLOGY, 2012, 313 : 61 - 67
  • [4] pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
    Cheng, Xiang
    Xiao, Xuan
    Chou, Kuo-Chen
    [J]. GENOMICS, 2018, 110 (04) : 231 - 239
  • [5] Gene ontology based transfer learning for protein subcellular localization
    Mei, Suyu
    Fei, Wang
    Zhou, Shuigeng
    [J]. BMC BIOINFORMATICS, 2011, 12
  • [6] Gene ontology based transfer learning for protein subcellular localization
    Suyu Mei
    Wang Fei
    Shuigeng Zhou
    [J]. BMC Bioinformatics, 12
  • [7] Prediction of protein subcellular localization by weighted gene ontology terms
    Chi, Sang-Mun
    [J]. BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, 2010, 399 (03) : 402 - 405
  • [8] BUSCA: an integrative web server to predict subcellular localization of proteins
    Savojardo, Castrense
    Martelli, Pier Luigi
    Fariselli, Piero
    Profiti, Giuseppe
    Casadio, Rita
    [J]. NUCLEIC ACIDS RESEARCH, 2018, 46 (W1) : W459 - W466
  • [9] CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information
    Zhang, Wei
    Xue, Xiaoli
    Xie, Chengwang
    Li, Yuanyuan
    Liu, Junhong
    Chen, Hailin
    Li, Guanghui
    [J]. INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2021, 13 (03) : 349 - 361
  • [10] CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information
    Wei Zhang
    Xiaoli Xue
    Chengwang Xie
    Yuanyuan Li
    Junhong Liu
    Hailin Chen
    Guanghui Li
    [J]. Interdisciplinary Sciences: Computational Life Sciences, 2021, 13 : 349 - 361