Literature DB >> 15513989

Predicting protein localization in budding yeast.

Kuo-Chen Chou1, Yu-Dong Cai.   

Abstract

MOTIVATION: Most of the existing methods in predicting protein subcellular location were used to deal with the cases limited within the scope from two to five localizations, and only a few of them can be effectively extended to cover the cases of 12-14 localizations. This is because the more the locations involved are, the poorer the success rate would be. Besides, some proteins may occur in several different subcellular locations, i.e. bear the feature of 'multiplex locations'. So far there is no method that can be used to effectively treat the difficult multiplex location problem. The present study was initiated in an attempt to address (1) how to efficiently identify the localization of a query protein among many possible subcellular locations, and (2) how to deal with the case of multiplex locations.
RESULTS: By hybridizing gene ontology, functional domain and pseudo amino acid composition approaches, a new method has been developed that can be used to predict subcellular localization of proteins with multiplex location feature. A global analysis of the proteins in budding yeast classified into 22 locations was performed by jack-knife cross-validation with the new method. The overall success identification rate thus obtained is 70%. In contrast to this, the corresponding rates obtained by some other existing methods were only 13-14%, indicating that the new method is very powerful and promising. Furthermore, predictions were made for the four proteins whose localizations could not be determined by experiments, as well as for the 236 proteins whose localizations in budding yeast were ambiguous according to experimental observations. However, according to our predicted results, many of these 'ambiguous proteins' were found to have the same score and ranking for several different subcellular locations, implying that they may simultaneously exist, or move around, in these locations. This finding is intriguing because it reflects the dynamic feature of these proteins in a cell that may be associated with some special biological functions.

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Year:  2004        PMID: 15513989     DOI: 10.1093/bioinformatics/bti104

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

1.  Using fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types.

Authors:  Hui Liu; Jie Yang; Meng Wang; Li Xue; Kuo-Chen Chou
Journal:  Protein J       Date:  2005-08       Impact factor: 2.371

2.  Prediction of compounds' biological function (metabolic pathways) based on functional group composition.

Authors:  Yu-Dong Cai; Ziliang Qian; Lin Lu; Kai-Yan Feng; Xin Meng; Bing Niu; Guo-Dong Zhao; Wen-Cong Lu
Journal:  Mol Divers       Date:  2008-08-14       Impact factor: 2.943

3.  Protein subcellular localization prediction of eukaryotes using a knowledge-based approach.

Authors:  Hsin-Nan Lin; Ching-Tai Chen; Ting-Yi Sung; Shinn-Ying Ho; Wen-Lian Hsu
Journal:  BMC Bioinformatics       Date:  2009-12-03       Impact factor: 3.169

4.  Amino acid biases in the N- and C-termini of proteins are evolutionarily conserved and are conserved between functionally related proteins.

Authors:  Peter M Palenchar
Journal:  Protein J       Date:  2008-08       Impact factor: 2.371

5.  Proteome-wide discovery of mislocated proteins in cancer.

Authors:  KiYoung Lee; Kyunghee Byun; Wonpyo Hong; Han-Yu Chuang; Chan-Gi Pack; Enkhjargal Bayarsaikhan; Sun Ha Paek; Hyosil Kim; Hye Young Shin; Trey Ideker; Bonghee Lee
Journal:  Genome Res       Date:  2013-05-14       Impact factor: 9.043

6.  Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

Authors:  Yu-Fei Gao; Lei Chen; Yu-Dong Cai; Kai-Yan Feng; Tao Huang; Yang Jiang
Journal:  PLoS One       Date:  2012-09-21       Impact factor: 3.240

7.  Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

Authors:  Jonathan Q Jiang; Maoying Wu
Journal:  BMC Bioinformatics       Date:  2012-06-25       Impact factor: 3.169

8.  PNAC: a protein nucleolar association classifier.

Authors:  Michelle S Scott; François-Michel Boisvert; Angus I Lamond; Geoffrey J Barton
Journal:  BMC Genomics       Date:  2011-01-27       Impact factor: 3.969

9.  Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.

Authors:  Jianjun He; Hong Gu; Wenqi Liu
Journal:  PLoS One       Date:  2012-06-08       Impact factor: 3.240

10.  mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines.

Authors:  Shibiao Wan; Man-Wai Mak; Sun-Yuan Kung
Journal:  BMC Bioinformatics       Date:  2012-11-06       Impact factor: 3.169

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