Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition.

Ion channels are integral membrane proteins that control movement of ions into or out of cells. They are key components in a wide range of biological processes. Different types of ion channels have different biological functions. With the appearance of vast proteomic data, it is highly desirable for both basic research and drug-target discovery to develop a computational method for the reliable prediction of ion channels and their types. In this study, we developed a support vector machine-based method to predict ion channels and their types using primary sequence information. A feature selection technique, analysis of variance (ANOVA), was introduced to remove feature redundancy and find out an optimized feature set for improving predictive performance. Jackknife cross-validated results show that the proposed method can discriminate ion channels from non-ion channels with an overall accuracy of 86.6%, classify voltage-gated ion channels and ligand-gated ion channels with an overall accuracy of 92.6% and predict four types (potassium, sodium, calcium and anion) of voltage-gated ion channels with an overall accuracy of 87.8%, respectively. These results indicate that the proposed method can correctly identify ion channels and provide important instructions for drug-target discovery. The predictor can be freely downloaded from http://cobi.uestc.edu.cn/people/hlin/tools/IonchanPred/.

[1]  Kuo-Chen Chou,et al.  Support vector machines for the classification and prediction of β‐turn types , 2002, Journal of peptide science : an official publication of the European Peptide Society.

[2]  Adam Godzik,et al.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..

[3]  Carmay Lim,et al.  Determinants of K+ vs Na+ selectivity in potassium channels. , 2009, Journal of the American Chemical Society.

[4]  Kuo-Chen Chou,et al.  Designing Inhibitors of M2 Proton Channel against H1N1 Swine Influenza Virus , 2010, PloS one.

[5]  Yanzhi Guo,et al.  Local sequence information-based support vector machine to classify voltage-gated potassium channels. , 2006, Acta biochimica et biophysica Sinica.

[6]  Peng Sun,et al.  Predict potential drug targets from the ion channel proteins based on SVM. , 2010, Journal of theoretical biology.

[7]  J. Chou,et al.  Structure and mechanism of the M2 proton channel of influenza A virus , 2008, Nature.

[8]  C. Morton,et al.  Ion Channel Gene Expression in the Inner Ear , 2007, Journal of the Association for Research in Otolaryngology.

[9]  R. Kass,et al.  The channelopathies: novel insights into molecular and genetic mechanisms of human disease. , 2005, The Journal of clinical investigation.

[10]  J. Nieto,et al.  Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. , 2009, Journal of theoretical biology.

[11]  Q Gu,et al.  Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns. , 2010, Protein and peptide letters.

[12]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[13]  Shandar Ahmad,et al.  Application of residue distribution along the sequence for discriminating outer membrane proteins , 2005, Comput. Biol. Chem..

[14]  Kuo-Chen Chou,et al.  Identification of proteases and their types. , 2009, Analytical biochemistry.

[15]  Gajendra P. S. Raghava,et al.  VGIchan : Prediction and Classification of Voltage-Gated Ion Channels , 2012 .

[16]  Kuo-Chen Chou,et al.  Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus. , 2009, Journal of theoretical biology.

[17]  Kuo-Chen Chou,et al.  Insights from studying the mutation-induced allostery in the M2 proton channel by molecular dynamics. , 2010, Protein engineering, design & selection : PEDS.

[18]  K. Chou,et al.  REVIEW : Recent advances in developing web-servers for predicting protein attributes , 2009 .

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Xiaoying Jiang,et al.  Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. , 2008, Protein and peptide letters.

[21]  Paul Horton,et al.  Discrimination of outer membrane proteins using support vector machines , 2005, Bioinform..

[22]  K. Chou,et al.  Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.

[23]  Kuo-Chen Chou.  Insights from modeling three-dimensional structures of the human potassium and sodium channels. , 2004, Journal of proteome research.

[24]  Kuo-Chen Chou,et al.  Support vector machines for prediction of protein signal sequences and their cleavage sites , 2003, Peptides.

[25]  Kuo-Chen Chou,et al.  GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. , 2009, Protein engineering, design & selection : PEDS.

[26]  Hao Lin,et al.  Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition , 2009, Acta biotheoretica.

[27]  Li Yang,et al.  Using auto covariance method for functional discrimination of membrane proteins based on evolution information , 2009, Amino Acids.

[28]  Hao Lin,et al.  Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. , 2009, Protein and peptide letters.

[29]  Jianding Qiu,et al.  Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. , 2009, Analytical biochemistry.

[30]  Wataru Nemoto,et al.  GRIP: A server for predicting interfaces for GPCR oligomerization , 2009, Journal of receptor and signal transduction research.

[31]  J. Chou,et al.  Mechanism of drug inhibition and drug resistance of influenza A M2 channel , 2009, Proceedings of the National Academy of Sciences.

[32]  Kuo-Chen Chou,et al.  Identify catalytic triads of serine hydrolases by support vector machines. , 2004, Journal of theoretical biology.

[33]  A. Liantonio,et al.  Therapeutic approaches to ion channel diseases. , 2008, Advances in genetics.

[34]  Jenn-Kang Hwang,et al.  Prediction of protein subcellular localization , 2006, Proteins.

[35]  Xiaoyong Zou,et al.  Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. , 2009, Protein and peptide letters.

[36]  Hao Lin,et al.  Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. , 2008, Protein and peptide letters.

[37]  Zhanchao Li,et al.  Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. , 2007, Journal of theoretical biology.

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

[39]  Kuo-Chen Chou,et al.  A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 , 2010, PloS one.

[40]  Hao Lin.  The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. , 2008, Journal of theoretical biology.

[41]  J. Chou,et al.  Solution structure and functional analysis of the influenza B proton channel , 2009, Nature Structural &Molecular Biology.

[42]  Xiaohe Shi,et al.  Prediction of interactiveness of proteins and nucleic acids based on feature selections , 2010, Molecular Diversity.

[43]  Eslam Pourbasheer,et al.  Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. , 2009, European journal of medicinal chemistry.

[44]  Kuo-Chen Chou,et al.  Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. , 2008, Journal of theoretical biology.

[45]  K. Chou,et al.  Application of SVM to predict membrane protein types. , 2004, Journal of theoretical biology.

[46]  Guangya Zhang,et al.  Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. , 2008, Journal of theoretical biology.

[47]  Kuo-Chen Chou,et al.  MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. , 2007, Biochemical and biophysical research communications.

[48]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  K. Chou,et al.  Prediction of protein secondary structure content. , 1999, Protein engineering.

[50]  Burkhard Rost,et al.  Sequence conserved for subcellular localization , 2002, Protein science : a publication of the Protein Society.

[51]  Peter Willett,et al.  Prediction of Ion Channel Activity Using Binary Kernel Discrimination , 2007, J. Chem. Inf. Model..

[52]  Kuo-Chen Chou,et al.  Investigation into adamantane-based M2 inhibitors with FB-QSAR. , 2009, Medicinal chemistry (Shariqah (United Arab Emirates)).

[53]  K. Chou,et al.  Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization , 2010, PloS one.

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

[55]  K. Chou,et al.  Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features , 2010, PloS one.

[56]  Jeffrey J Clare,et al.  Targeting ion channels for drug discovery. , 2010, Discovery medicine.

[57]  Kuo-Chen Chou,et al.  Insights from investigating the interactions of adamantane-based drugs with the M2 proton channel from the H1N1 swine virus. , 2009, Biochemical and biophysical research communications.

[58]  Jia He,et al.  Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. , 2010, Analytical biochemistry.

[59]  Tong Wang,et al.  Using the nonlinear dimensionality reduction method for the prediction of subcellular localization of Gram-negative bacterial proteins , 2009, Molecular Diversity.

[60]  Jian-Ding Qiu,et al.  Prediction of the Types of Membrane Proteins Based on Discrete Wavelet Transform and Support Vector Machines , 2010, The protein journal.

[61]  Kuo-Chen Chou,et al.  GPCR‐CA: A cellular automaton image approach for predicting G‐protein–coupled receptor functional classes , 2009, J. Comput. Chem..

[62]  Kuo-Chen Chou,et al.  Prediction of Protein Structural Classes by Support Vector Machines , 2002, Comput. Chem..

[63]  Diana Conte Camerino,et al.  Ion channel pharmacology , 2007, Neurotherapeutics.

[64]  Kuo-Chen Chou,et al.  Support vector machines for predicting HIV protease cleavage sites in protein , 2002, J. Comput. Chem..

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

[66]  M. Esmaeili,et al.  Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. , 2010, Journal of theoretical biology.

[67]  M. Michael Gromiha,et al.  A simple statistical method for discriminating outer membrane proteins with better accuracy , 2005, Bioinform..

[68]  Kuo-Chen Chou,et al.  A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. , 2009, Analytical biochemistry.

[69]  Kuo-Chen Chou,et al.  An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus. , 2008, Biochemical and biophysical research communications.

[70]  Hao Lin,et al.  Prediction of subcellular location of mycobacterial protein using feature selection techniques , 2010, Molecular Diversity.

[71]  Rafal M. Pielak,et al.  Flu channel drug resistance: a tale of two sites , 2010, Protein & Cell.

[72]  Alan S. Verkman,et al.  Chloride channels as drug targets , 2009, Nature Reviews Drug Discovery.

[73]  Bertil Hille,et al.  Ion channels: From idea to reality , 1999, Nature Medicine.

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

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

[76]  K. Chou,et al.  Support vector machines for predicting the specificity of GalNAc-transferase , 2002, Peptides.

[77]  Kuo-Chen Chou,et al.  Predicting membrane protein types by the LLDA algorithm. , 2008, Protein and peptide letters.

[78]  Yanzhi Guo,et al.  Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. , 2009, Journal of theoretical biology.

[79]  K. Chou.  Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology , 2009 .

[80]  K. Chou,et al.  ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. , 2008, Biochemical and biophysical research communications.

[81]  Fengmin Li,et al.  Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. , 2008, Protein and peptide letters.

[82]  Kuo-Chen Chou,et al.  Support Vector Machine for predicting α-turn types , 2003, Peptides.