標題: PSLOoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysis
作者: Chang, Jia-Ming
Su, Emily Chia-Yu
Lo, Allan
Chiu, Hua-Sheng
Sung, Ting-Yi
Hsu, Wen-Lian
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: protein subcellular localization;document classification;vector space model;gapped-dipeptides;probabilistic latent semantic analysis;support vector machines
公開日期: 1-八月-2008
摘要: Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836-2847, Yu et al., Proteins 2006;64:643-651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.219% respectively, and it compares favorably with that of CELLO H (Yu et al., Proteins 2006,64:643-651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005,21:617-623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/similar to bioapp/PSLDoc/.
URI: http://dx.doi.org/10.1002/prot.21944
http://hdl.handle.net/11536/8489
ISSN: 0887-3585
DOI: 10.1002/prot.21944
期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume: 72
Issue: 2
起始頁: 693
結束頁: 710
顯示於類別:期刊論文


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