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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics

Open Access Proceedings

Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

Hsin-Nan Lin123, Ching-Tai Chen123, Ting-Yi Sung2, Shinn-Ying Ho3 and Wen-Lian Hsu2*

Author Affiliations

1 Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan, Republic of China

2 Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan, Republic of China

3 Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan, Republic of China

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BMC Bioinformatics 2009, 10(Suppl 15):S8  doi:10.1186/1471-2105-10-S15-S8

Published: 3 December 2009

Abstract

Background

The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.

Results

In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPredsite's performance. The experiment results show that KnowPredsite achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPredsite is 91.7%. For multi-localized proteins, the overall accuracy of KnowPredsite is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPredsite.

Conclusion

KnowPredsite demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPredsite is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPredsite is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPredsite prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/ webcite.