Predicting System for Subcellular Location of Proteins

Subcellular location relates closely with protein function, which is the key attribute of gene production. It is well known that the prediction of subcellular location plays a crucial role in many aspects of molecular biology. Hence, the development of one new algorithm to rapidly forecast which subcellular location of a new protein sequence will locate in is extremely useful.The proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic raticulum, (5) extracell, (6) Golgi apparatus, (7) lysosomal, (8) mitochondrial, (9) nuclear, (10) peroxisomal, (11) plasma membrane, and (12) vacuole, which have covered almost all the organelles and subcellular compartments in an animal or plant cell.

The method of subcellular location type prediction provided in this web-site is based on the AdaBoost Learner Algorithm incorporated with amino acid composition.

See more details in our paper: Predicting subcellular localization with AdaBoost Learner; Yu-huan Jin1, Bing Niu2, Kai-Yan Feng5， Wen-Cong Lu1,2*，Yu-Dong Cai1,3**,Guo-Zheng Li4; Protein & Peptide Letters(Accepted).

 
Proteins' code :
Next to the symbol ">" is the name; next to the symbol "(" is the code of the protein. And there must be existed a blank between the name and the code.

 

 

 

 

 

 

 

E-mail: wclu@shu.edu.cn

Laboratory of Chemical Data Mining, Shanghai University