Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform

Summary.As the potential drug targets, G-protein coupled receptors (GPCRs) and nuclear receptors (NRs) are the focuses in pharmaceutical research. It is of great practical significance to develop an automated and reliable method to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine was proposed to classify GPCRs and NRs from the hydrophobicity of proteins. The models for all the GPCR families and NR subfamilies were trained and validated using jackknife test and the results thus obtained are quite promising. Meanwhile, the performance of the method was evaluated on GPCR and NR independent datasets with good performance. The good results indicate the applicability of the method. Two web servers implementing the prediction are available at http://chem.scu.edu.cn/blast/Pred-GPCR and http://chem.scu.edu.cn/blast/Pred-NR.

[1]  Michael F. Shlesinger,et al.  WAVELET TRANSFORMATION OF PROTEIN HYDROPHOBICITY SEQUENCES SUGGESTS THEIR MEMBERSHIPS IN STRUCTURAL FAMILIES , 1997 .

[2]  D. Lipman,et al.  Improved tools for biological sequence comparison. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[3]  David Haussler,et al.  Classifying G-protein coupled receptors with support vector machines , 2002, Bioinform..

[4]  R. Grantham.  Amino Acid Difference Formula to Help Explain Protein Evolution , 1974, Science.

[5]  Yanzhi Guo,et al.  Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies. , 2005, Acta biochimica et biophysica Sinica.

[6]  Gajendra P S Raghava,et al.  Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* , 2004, Journal of Biological Chemistry.

[7]  Z. Huang,et al.  Using pseudo amino acid composition to predict protein subcellular location: Approached with Lyapunov index, Bessel function, and Chebyshev filter , 2005, Amino Acids.

[8]  Masami Ikeda,et al.  Proteome-wide classification and identification of mammalian-type GPCRs by binary topology pattern , 2004, Comput. Biol. Chem..

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

[10]  R. Neubig,et al.  Depicting a protein's two faces: GPCR classification by phylogenetic tree‐based HMMs , 2003, FEBS letters.

[11]  K. Chou,et al.  Bioinformatical analysis of G-protein-coupled receptors. , 2002, Journal of proteome research.

[12]  K. Chou,et al.  Support vector machines for predicting membrane protein types by using functional domain composition. , 2003, Biophysical journal.

[13]  Guo-Ping Zhou,et al.  Subcellular location prediction of apoptosis proteins , 2002, Proteins.

[14]  Shoshi Kikuchi,et al.  The Rice PIPELINE: a unification tool for plant functional genomics , 2004, Nucleic Acids Res..

[15]  Marjana Novic,et al.  Investigation of Infrared Spectra-Structure Correlation Using Kohonen and Counterpropagation Neural Network , 1995, J. Chem. Inf. Comput. Sci..

[16]  Gert Vriend,et al.  GPCRDB information system for G protein-coupled receptors , 2003, Nucleic Acids Res..

[17]  M. Wang,et al.  Low-frequency Fourier spectrum for predicting membrane protein types. , 2005, Biochemical and biophysical research communications.

[18]  I. Cosic.  Macromolecular bioactivity: is it resonant interaction between macromolecules?-theory and applications , 1994, IEEE Transactions on Biomedical Engineering.

[19]  Kuo-Chen Chou,et al.  Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. , 2005, Biochemical and biophysical research communications.

[20]  K. Chou,et al.  Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.

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

[22]  Kuo-Chen Chou,et al.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..

[23]  K. Chou,et al.  A study on the correlation of G-protein-coupled receptor types with amino acid composition. , 2002, Protein engineering.

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

[25]  H. Gronemeyer,et al.  Transcription factors 3: nuclear receptors. , 1995, Protein profile.

[26]  T. Kikuchi,et al.  Construction of Hypothetical Three-Dimensional Structure of P2Y1 Receptor Based on Fourier Transform Analysis , 2002, Journal of protein chemistry.

[27]  K. Palczewski,et al.  Crystal Structure of Rhodopsin: A G‐Protein‐Coupled Receptor , 2000, Science.

[28]  Kuo-Chen Chou,et al.  Predicting protein structural class by functional domain composition. , 2004, Biochemical and biophysical research communications.

[29]  K. Chou,et al.  Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location* , 2002, The Journal of Biological Chemistry.

[30]  Kuo-Chen Chou,et al.  Prediction of G-protein-coupled receptor classes. , 2005, Journal of proteome research.

[31]  Robert D. Finn,et al.  The Pfam protein families database , 2004, Nucleic Acids Res..

[32]  Gert Vriend,et al.  Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems , 2001, Nucleic Acids Res..

[33]  K. Chou.  A novel approach to predicting protein structural classes in a (20–1)‐D amino acid composition space , 1995, Proteins.

[34]  T. Gudermann,et al.  Receptors and G proteins as primary components of transmembrane signal transduction , 1995, Journal of Molecular Medicine.

[35]  Qiang Fang,et al.  Protein sequence comparison based on the wavelet transform approach. , 2002, Protein engineering.

[36]  K. Katoh,et al.  MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. , 2002, Nucleic acids research.

[37]  T. Gudermann,et al.  Receptors and G proteins as primary components of transmembrane signal transduction , 1995, Journal of Molecular Medicine.

[38]  K. Chou,et al.  Low-frequency collective motion in biomacromolecules and its biological functions. , 1988, Biophysical chemistry.

[39]  Kuo-Chen Chou,et al.  Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. , 2005, Biochemical and biophysical research communications.

[40]  K. Chou,et al.  Prediction of protein signal sequences and their cleavage sites by statistical rulers. , 2005, Biochemical and biophysical research communications.

[41]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[42]  Kuo-Chen Chou,et al.  Using functional domain composition to predict enzyme family classes. , 2005, Journal of proteome research.

[43]  Denise Gorse,et al.  A novel approach to the recognition of protein architecture from sequence using fourier analysis and neural networks , 2002, Proteins.

[44]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[45]  Z. Huang,et al.  Using cellular automata images and pseudo amino acid composition to predict protein subcellular location , 2005, Amino Acids.

[46]  M. Wang,et al.  Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. , 2004, Protein engineering, design & selection : PEDS.

[47]  Robert C. Edgar,et al.  Local homology recognition and distance measures in linear time using compressed amino acid alphabets. , 2004, Nucleic acids research.

[48]  Gajendra P. S. Raghava,et al.  GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors , 2004, Nucleic Acids Res..

[49]  Guo-Ping Zhou,et al.  An Intriguing Controversy over Protein Structural Class Prediction , 1998, Journal of protein chemistry.

[50]  Vasilis J. Promponas,et al.  PRED-GPCR: GPCR recognition and family classification server , 2004, Nucleic Acids Res..

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

[52]  V. Laudet,et al.  Bioinformatics of nuclear receptors. , 2003, Methods in enzymology.

[53]  G P Zhou,et al.  Some insights into protein structural class prediction , 2001, Proteins.

[54]  K. Umesono,et al.  The nuclear receptor superfamily: The second decade , 1995, Cell.

[55]  Jun Cai,et al.  Classifying G-protein coupled receptors with bagging classification tree , 2004, Comput. Biol. Chem..