Identification of Multiple Subcellular Locations for Proteins in Budding Yeast

被引:22
|
作者
Wan, Si-Bao [2 ]
Hu, Le-Le [1 ,3 ]
Niu, Sheng [4 ]
Wang, Kai [1 ]
Cai, Yu-Dong [1 ,5 ]
Lu, Wen-Cong [3 ]
Chou, Kuo-Chen [5 ]
机构
[1] Shanghai Univ, Inst Syst Biol, Shanghai 200444, Peoples R China
[2] Shanghai Univ, Sch Life Sci, Shanghai Key Lab Bioenergy Crops, Shanghai 200444, Peoples R China
[3] Shanghai Univ, Coll Sci, Dept Chem, Shanghai 200444, Peoples R China
[4] Chinese Acad Sci, Key Lab Syst Biol, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China
[5] Gordon Life Sci Inst, San Diego, CA 92130 USA
基金
中国国家自然科学基金;
关键词
Multi subcellular locations; incremental feature selection; sort-PLoc; AMINO-ACID-COMPOSITION; SUPPORT VECTOR MACHINE; OUTER-MEMBRANE PROTEINS; STRUCTURAL CLASS; DRUG-METABOLISM; TOPOLOGICAL INDEXES; FEATURE-SELECTION; COMPLEX NETWORKS; GENE ONTOLOGY; PHARMACEUTICAL DESIGN;
D O I
10.2174/157489311795222374
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Knowing the subcellular locations of a protein helps to explore its functions in vivo since a protein can only play its roles properly if and only if it is located at certain subcellular compartments. Since it is both time-consuming and costly to determine protein subcellular localization purely by means of the conventional biotechnology experiments, computational methods play an important complementary role in this regard. Although a number of computational methods have been developed for predicting protein subcellular localization, it remains a challenge to deal with the multiplex proteins that may simultaneously exist at, or move between, two or more different locations. Here, a new predictor called Sort-PLoc was developed to tackle such a difficult and challenging problem. The key step was to select protein domains to code the protein samples by Incremental Feature Selection method. In each prediction, a series of subcellular locations were sorted descendingly according to their likelihood to be the site where the query protein may reside. Based on the selected domain set, the importance of Gene Ontology (GO) terms and domains in the contribution to the prediction was analyzed that may provide useful insights to the relevant areas. For the convenience of the broad experimental scientists, a user-friendly web-server for Sort-PLoc was established that is freely accessible to the public at http://yscl.biosino.org/.
引用
收藏
页码:71 / 80
页数:10
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