A method to improve protein subcellular localization prediction by integrating various biological data sources

BackgroundProtein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.ResultsIn this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.ConclusionOur results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.

[1]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[2]  Michael T. Hallett,et al.  Refining Protein Subcellular Localization , 2005, PLoS Comput. Biol..

[3]  Minoru Kanehisa,et al.  Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs , 2003, Bioinform..

[4]  Martin Reczko,et al.  Prediction of the subcellular localization of eukaryotic proteins using sequence signals and composition , 2004, Proteomics.

[5]  K. Chou,et al.  Using discriminant function for prediction of subcellular location of prokaryotic proteins. , 1998, Biochemical and biophysical research communications.

[6]  M. Kanehisa,et al.  A knowledge base for predicting protein localization sites in eukaryotic cells , 1992, Genomics.

[7]  S. Brunak,et al.  Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. , 2000, Journal of molecular biology.

[8]  T. Hubbard,et al.  Using neural networks for prediction of the subcellular location of proteins. , 1998, Nucleic acids research.

[9]  Paul Horton,et al.  Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier , 1997, ISMB.

[10]  E. O’Shea,et al.  Global analysis of protein localization in budding yeast , 2003, Nature.

[11]  Paul Horton,et al.  Nucleic Acids Research Advance Access published May 21, 2007 WoLF PSORT: protein localization predictor , 2007 .

[12]  S. Brunak,et al.  SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites , 1997 .

[13]  Hagit Shatkay,et al.  SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data. , 2007, Bioinformatics.

[14]  Zhirong Sun,et al.  Support vector machine approach for protein subcellular localization prediction , 2001, Bioinform..

[15]  Kuo-Chen Chou,et al.  Predicting protein localization in budding Yeast , 2005, Bioinform..

[16]  Ying Huang,et al.  Prediction of protein subcellular locations using fuzzy k-NN method , 2004, Bioinform..

[17]  Kuo-Chen Chou,et al.  Predicting subcellular localization of proteins in a hybridization space , 2004, Bioinform..

[18]  Jian Guo,et al.  A novel method for protein subcellular localization: Combining residue-couple model and SVM , 2005, APBC.

[19]  Emily Dimmer,et al.  The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology , 2004, Nucleic Acids Res..

[20]  Paul Horton,et al.  PROTEIN SUBCELLULAR LOCALIZATION PREDICTION WITH WOLF PSORT , 2005 .

[21]  M Gerstein,et al.  Genome-wide analysis relating expression level with protein subcellular localization. , 2000, Trends in genetics : TIG.

[22]  Shiow-Fen Hwang,et al.  ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization , 2008, BMC Bioinformatics.

[23]  Doheon Lee,et al.  PLPD: reliable protein localization prediction from imbalanced and overlapped datasets , 2006, Nucleic acids research.

[24]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 , 2000, Nucleic Acids Res..

[25]  P. Aloy,et al.  Relation between amino acid composition and cellular location of proteins. , 1997, Journal of molecular biology.

[26]  Kuo-Chen Chou,et al.  A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology. , 2003, Biochemical and biophysical research communications.

[27]  G. Heijne,et al.  ChloroP, a neural network‐based method for predicting chloroplast transit peptides and their cleavage sites , 1999, Protein science : a publication of the Protein Society.

[28]  K. Chou,et al.  Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. , 2006, Biochemical and biophysical research communications.

[29]  Jenn-Kang Hwang,et al.  Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions , 2004, Protein science : a publication of the Protein Society.

[30]  Kuo-Chen Chou,et al.  Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo‐amino acid composition , 2004, Journal of cellular biochemistry.

[31]  E. Marcotte,et al.  An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae , 2007, PloS one.