Garg, Aarti ; Bhasin, Manoj ; Raghava, Gajendra P. S. (2005) Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search Journal of Biological Chemistry, 280 (15). pp. 14427-14432. ISSN 0021-9258
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Official URL: http://www.jbc.org/content/280/15/14427.short
Related URL: http://dx.doi.org/10.1074/jbc.M411789200
Abstract
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.
Item Type: | Article |
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Source: | Copyright of this article belongs to The American Society for Biochemistry and Molecular Biology. |
ID Code: | 37201 |
Deposited On: | 25 Apr 2011 13:06 |
Last Modified: | 17 May 2016 20:06 |
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