TY  - JOUR
N1  - Copyright of this article belongs to ASBMB.
ID  - open193
UR  - http://www.jbc.org/content/280/15/14427.long
IS  - 15
A1  - Garg, Aarti
A1  - Bhasin, Manoj
A1  - Raghava, G.P.S.
Y1  - 2005/04/15/
N2  - Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.
PB  - ASBMB
JF  - The Journal of biological chemistry
VL  - 280
SN  - 0021-9258
TI  - Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search.
SP  - 14427
AV  - restricted
EP  - 32
ER  -