Journal of Computer-Aided Molecular Design

, Volume 27, Issue 1, pp 91–103 | Cite as

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

  • Tzu-Hao Chang
  • Li-Ching Wu
  • Tzong-Yi Lee
  • Shu-Pin Chen
  • Hsien-Da Huang
  • Jorng-Tzong Horng


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


Subcellular localization Protein function Eukaryote Support vector machine 



The authors would like to thank the National Science Council of the Republic of China, No. NSC 101-2628-E-155-002-MY2, 99-2221-E-008-083-MY3, NSC 101-2311-B-009-003-MY3 and NSC 100-2627-B-009-002. This work was supported in part by the UST-UCSD International Center of Excellence in Advanced Bioengineering sponsored by the Taiwan National Science Council I-RiCE Program under Grant Number: NSC 101-2911-I-009-101, and Veterans General Hospitals and University System of Taiwan (VGHUST) Joint Research Program under Grant Number: VGHUST101-G5-1-1. This work was also partially supported by MOE ATU.

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Tzu-Hao Chang
    • 1
  • Li-Ching Wu
    • 2
  • Tzong-Yi Lee
    • 3
  • Shu-Pin Chen
    • 4
  • Hsien-Da Huang
    • 5
    • 6
  • Jorng-Tzong Horng
    • 2
    • 4
    • 7
  1. 1.Graduate Institute of Biomedical InformaticsTaipei Medical UniversityTaipeiTaiwan
  2. 2.Institute of Systems Biology and BioinformaticsNational Central UniversityChung-LiTaiwan
  3. 3.Department of Computer Science and EngineeringYuan Ze UniversityChung-LiTaiwan
  4. 4.Department of Computer Science and Information EngineeringNational Central UniversityChung-LiTaiwan
  5. 5.Institute of Bioinformatics and Systems BiologyNational Chiao Tung UniversityHsin-ChuTaiwan
  6. 6.Department of Biological Science and TechnologyNational Chiao Tung UniversityHsin-ChuTaiwan
  7. 7.Department of Biomedical InformaticsAsia UniversityWufengTaiwan

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