標題: EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC
作者: Chang, Tzu-Hao
Wu, Li-Ching
Lee, Tzong-Yi
Chen, Shu-Pin
Huang, Hsien-Da
Horng, Jorng-Tzong
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: Subcellular localization;Protein function;Eukaryote;Support vector machine
公開日期: 1-一月-2013
摘要: The function of a protein is generally related to its subcellular localization. Therefore, knowing its subcellular localization is helpful in understanding its potential functions and roles in biological processes. This work develops a hybrid method for computationally predicting the subcellular localization of eukaryotic protein. The method is called EuLoc and incorporates the Hidden Markov Model (HMM) method, homology search approach and the support vector machines (SVM) method by fusing several new features into Chou's pseudo-amino acid composition. The proposed SVM module overcomes the shortcoming of the homology search approach in predicting the subcellular localization of a protein which only finds low-homologous or non-homologous sequences in a protein subcellular localization annotated database. The proposed HMM modules overcome the shortcoming of SVM in predicting subcellular localizations using few data on protein sequences. Several features of a protein sequence are considered, including the sequence-based features, the biological features derived from PROSITE, NLSdb and Pfam, the post-transcriptional modification features and others. The overall accuracy and location accuracy of EuLoc are 90.5 and 91.2 %, respectively, revealing a better predictive performance than obtained elsewhere. Although the amounts of data of the various subcellular location groups in benchmark dataset differ markedly, the accuracies of 12 subcellular localizations of EuLoc range from 82.5 to 100 %, indicating that this tool is much more balanced than other tools. EuLoc offers a high, balanced predictive power for each subcellular localization. EuLoc is now available on the web at http://euloc.mbc.nctu.edu.tw/.
URI: http://dx.doi.org/10.1007/s10822-012-9628-0
http://hdl.handle.net/11536/21284
ISSN: 0920-654X
DOI: 10.1007/s10822-012-9628-0
期刊: JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Volume: 27
Issue: 1
起始頁: 91
結束頁: 103
顯示於類別:期刊論文


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  1. 000315045400007.pdf