Support vector machine approach for protein subcellular localization prediction

MOTIVATION Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.

[1]  M.M. Van Hulle,et al.  View-based 3D object recognition with support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

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

[3]  B. Rost,et al.  Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.

[4]  B. Rost,et al.  Adaptation of protein surfaces to subcellular location. , 1998, Journal of molecular biology.

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

[6]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

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

[8]  Robert F. Murphy,et al.  Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images , 2000, ISMB.

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

[10]  Zheng Yuan.  Prediction of protein subcellular locations using Markov chain models , 1999, FEBS letters.

[11]  M. Gelfand,et al.  Starts of bacterial genes: estimating the reliability of computer predictions. , 1999, Gene.

[12]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Robert J. Vanderbei,et al.  Commentary - Interior-Point Methods: Algorithms and Formulations , 1994, INFORMS J. Comput..

[14]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[15]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[17]  K Nishikawa,et al.  Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. , 1994, Journal of molecular biology.

[18]  Søren Brunak,et al.  A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites , 1997, Int. J. Neural Syst..

[19]  K. Nakai.  Protein sorting signals and prediction of subcellular localization. , 2000, Advances in protein chemistry.

[20]  H Nielsen,et al.  Machine learning approaches for the prediction of signal peptides and other protein sorting signals. , 1999, Protein engineering.

[21]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[22]  K. Chou,et al.  Protein subcellular location prediction. , 1999, Protein engineering.

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

[24]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

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

[26]  M. Kanehisa,et al.  Expert system for predicting protein localization sites in gram‐negative bacteria , 1991, Proteins.

[27]  M. Gerstein,et al.  A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome. , 2000, Journal of molecular biology.

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[30]  Pietro Liò,et al.  Wavelet change-point prediction of transmembrane proteins , 2000, Bioinform..

[31]  B. Rost,et al.  Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy , 1996, Protein science : a publication of the Protein Society.

[32]  P Bork,et al.  Wanted: subcellular localization of proteins based on sequence. , 1998, Trends in cell biology.

[33]  Shigeki Mitaku,et al.  SOSUI: classification and secondary structure prediction system for membrane proteins , 1998, Bioinform..