AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices

Summary.Identifying a protein’s subcellular localization is an important step to understand its function. However, the involved experimental work is usually laborious, time consuming and costly. Computational prediction hence becomes valuable to reduce the inefficiency. Here we provide a method to predict protein subcellular localization by using amino acid composition and physicochemical properties. The method concatenates the information extracted from a protein’s N-terminal, middle and full sequence. Each part is represented by amino acid composition, weighted amino acid composition, five-level grouping composition and five-level dipeptide composition. We divided our dataset into training and testing set. The training set is used to determine the best performing amino acid index by using five-fold cross validation, whereas the testing set acts as the independent dataset to evaluate the performance of our model. With the novel representation method, we achieve an accuracy of approximately 75% on independent dataset. We conclude that this new representation indeed performs well and is able to extract the protein sequence information. We have developed a web server for predicting protein subcellular localization. The web server is available at http://aaindexloc.bii.a-star.edu.sg.

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

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

[3]  Kuo-Chen Chou,et al.  Large‐scale plant protein subcellular location prediction , 2007, Journal of cellular biochemistry.

[4]  Z. Feng,et al.  Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition. , 2001, Biopolymers.

[5]  Hao Lin,et al.  Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. , 2007, Biochemical and biophysical research communications.

[6]  I. Shimada,et al.  N-terminal half of a mitochondrial presequence peptide takes a helical conformation when bound to dodecylphosphocholine micelles: a proton nuclear magnetic resonance study. , 1989, Journal of biochemistry.

[7]  Z. Huang,et al.  Using complexity measure factor to predict protein subcellular location , 2005, Amino Acids.

[8]  S.-W. Zhang,et al.  Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion , 2006, Amino Acids.

[9]  S.-W. Zhang,et al.  Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition , 2007, Amino Acids.

[10]  Yen-Han Lin,et al.  False positive reduction in protein-protein interaction predictions using gene ontology annotations , 2007, BMC Bioinformatics.

[11]  Ying-Li Chen,et al.  Prediction of the subcellular location of apoptosis proteins. , 2007, Journal of theoretical biology.

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

[13]  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.

[14]  Kuo-Chen Chou,et al.  Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[15]  Meng Wang,et al.  Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types , 2005, The protein journal.

[16]  K C Chou,et al.  Prediction of protein structural classes and subcellular locations. , 2000, Current protein & peptide science.

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

[18]  Kuo-Chen Chou,et al.  Prediction of Membrane Protein Types by Incorporating Amphipathic Effects , 2005, J. Chem. Inf. Model..

[19]  K. Chou,et al.  Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. , 2007, Protein engineering, design & selection : PEDS.

[20]  Scott Dick,et al.  Classifier ensembles for protein structural class prediction with varying homology. , 2006, Biochemical and biophysical research communications.

[21]  K. Chou,et al.  Prediction of membrane protein types and subcellular locations , 1999, Proteins.

[22]  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.

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

[24]  Kuo-Chen Chou,et al.  Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. , 2006, Journal of theoretical biology.

[25]  G. Li,et al.  Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform , 2006, Amino Acids.

[26]  K. Chou,et al.  Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. , 2007, Journal of proteome research.

[27]  C. Zhang,et al.  Predicting protein folding types by distance functions that make allowances for amino acid interactions. , 1994, The Journal of biological chemistry.

[28]  Qianzhong Li,et al.  Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components , 2007, J. Comput. Chem..

[29]  Peixiang Cai,et al.  Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. , 2006, Analytical biochemistry.

[30]  H.-B. Shen,et al.  Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction , 2007, Amino Acids.

[31]  X.-D. Sun,et al.  Prediction of protein structural classes using support vector machines , 2006, Amino Acids.

[32]  Xiangjun Liu,et al.  GNBSL: A new integrative system to predict the subcellular location for Gram‐negative bacteria proteins , 2006, Proteomics.

[33]  R. Herrmann,et al.  Protein import into chloroplasts. The hydrophilic lumenal proteins exhibit unexpected import and sorting specificities in spite of structurally conserved transit peptides. , 1993, The Journal of biological chemistry.

[34]  Kuo-Chen Chou,et al.  Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. , 2006, Journal of theoretical biology.

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

[36]  Yang Dai,et al.  An SVM-based system for predicting protein subnuclear localizations , 2005, BMC Bioinformatics.

[37]  Z. Wen,et al.  Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition , 2007, Amino Acids.

[38]  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.

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

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

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

[42]  Kuo-Chen Chou,et al.  Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor , 2006, J. Comput. Chem..

[43]  Jishou Ruan,et al.  Novel scales based on hydrophobicity indices for secondary protein structure. , 2007, Journal of theoretical biology.

[44]  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.

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

[46]  Sukanta Mondal,et al.  Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. , 2006, Journal of theoretical biology.

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

[48]  Jean-Philippe Vert,et al.  A novel representation of protein sequences for prediction of subcellular location using support vector machines , 2005, Protein science : a publication of the Protein Society.

[49]  Bhaskar D. Kulkarni,et al.  Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM , 2007, Pattern Recognit. Lett..

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

[51]  Enrico Schleiff,et al.  Protein import into chloroplasts , 2004, Nature Reviews Molecular Cell Biology.

[52]  Howard Leung,et al.  Prediction of membrane protein types from sequences and position-specific scoring matrices. , 2007, Journal of theoretical biology.

[53]  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.

[54]  K. Keegstra,et al.  Protein Import and Routing Systems of Chloroplasts , 1999, Plant Cell.

[55]  K. Chou,et al.  Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. , 2007, Biopolymers.

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

[57]  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.

[58]  K.-C. Chou,et al.  Using string kernel to predict signal peptide cleavage site based on subsite coupling model , 2005, Amino Acids.

[59]  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.

[60]  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.

[61]  Yoonkyung Lee,et al.  Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data , 2003, Bioinform..

[62]  Bernard F. Buxton,et al.  Secondary structure prediction with support vector machines , 2003, Bioinform..

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

[64]  Xiaoyong Zou,et al.  Using pseudo-amino acid composition and support vector machine to predict protein structural class. , 2006, Journal of theoretical biology.

[65]  Kuo-Chen Chou,et al.  Predicting protein subcellular location by fusing multiple classifiers , 2006, Journal of cellular biochemistry.

[66]  K. Nakai,et al.  PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. , 1999, Trends in biochemical sciences.

[67]  Chun Yan,et al.  Prediction of protein subcellular location using a combined feature of sequence , 2005, FEBS letters.

[68]  Yanda Li,et al.  Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence , 2006, BMC Bioinformatics.

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

[70]  K. Chou,et al.  Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. , 2000, Biochemical and biophysical research communications.

[71]  Ke Wang,et al.  PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteria , 2003, Nucleic Acids Res..

[72]  M. Bhasin,et al.  Support Vector Machine-based Method for Subcellular Localization of Human Proteins Using Amino Acid Compositions, Their Order, and Similarity Search* , 2005, Journal of Biological Chemistry.

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

[74]  J. Beechem,et al.  Bioinformatics methods for prioritizing serum biomarker candidates. , 2006, Clinical chemistry.

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

[76]  Ao Li,et al.  LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST , 2005, Nucleic Acids Res..

[77]  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.

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

[79]  Kuo-Chen Chou,et al.  Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. , 2006, Journal of proteome research.

[80]  Huanwen Tang,et al.  Prediction of Protein Subcellular Locations Using a New Measure of Information Discrepancy , 2005, J. Bioinform. Comput. Biol..

[81]  D. Gorenstein,et al.  Structure of the signal sequences for two mitochondrial matrix proteins that are not proteolytically processed upon import. , 1994, Biochemistry.

[82]  K. Chou,et al.  Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. , 2007, Biochemical and biophysical research communications.

[83]  Zhi-Ping Feng,et al.  An overview on predicting the subcellular location of a protein , 2002, Silico Biol..

[84]  Arun Krishnan,et al.  pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties , 2005, BMC Bioinformatics.

[85]  Oliver Kohlbacher,et al.  MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition , 2006, Bioinform..

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

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

[88]  C. Zhang,et al.  Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids. , 2001, International journal of biological macromolecules.

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

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

[91]  Gajendra P. S. Raghava,et al.  ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST , 2004, Nucleic Acids Res..

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

[93]  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.

[94]  Tongliang Zhang,et al.  Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes , 2007, Amino Acids.

[95]  Kuo-Chen Chou,et al.  Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. , 2007, Protein and peptide letters.

[96]  K. Chou,et al.  Prediction of linear B-cell epitopes using amino acid pair antigenicity scale , 2007, Amino Acids.

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

[98]  H.-B. Shen,et al.  Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments , 2006, Amino Acids.

[99]  H.-B. Shen,et al.  Using ensemble classifier to identify membrane protein types , 2006, Amino Acids.

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

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

[102]  Yongsheng Ding,et al.  Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence , 2006, Comput. Biol. Chem..

[103]  Lin He,et al.  Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach , 2003, Journal of protein chemistry.

[104]  Kuo-Chen Chou,et al.  Ensemble classifier for protein fold pattern recognition , 2006, Bioinform..

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

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

[107]  Kuo-Chen Chou,et al.  Predicting protein structural class with AdaBoost Learner. , 2006, Protein and peptide letters.