iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.

Predicting protein subcellular localization is a challenging problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multi-label proteins should not be ignored because they usually bear some special function worthy of in-depth studies. By introducing the "multi-label learning" approach, a new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure the prediction quality of a multi-label system in a rigorous way, five indices were introduced; they are "Absolute-True", "Absolute-False" (or Hamming-Loss"), "Accuracy", "Precision", and "Recall". As a demonstration, the jackknife cross-validation was performed with iLoc-Animal on a benchmark dataset of animal proteins classified into the following 20 location sites: (1) acrosome, (2) cell membrane, (3) centriole, (4) centrosome, (5) cell cortex, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracellular, (11) Golgi apparatus, (12) lysosome, (13) mitochondrion, (14) melanosome, (15) microsome, (16) nucleus, (17) peroxisome, (18) plasma membrane, (19) spindle, and (20) synapse, where many proteins belong to two or more locations. For such a complicated system, the outcomes achieved by iLoc-Animal for all the aforementioned five indices were quite encouraging, indicating that the predictor may become a useful tool in this area. It has not escaped our notice that the multi-label approach and the rigorous measurement metrics can also be used to investigate many other multi-label problems in molecular biology. As a user-friendly web-server, iLoc-Animal is freely accessible to the public at the web-site .

[1]  K Nishikawa,et al.  The folding type of a protein is relevant to the amino acid composition. , 1986, Journal of biochemistry.

[2]  John C. Wootton,et al.  Statistics of Local Complexity in Amino Acid Sequences and Sequence Databases , 1993, Comput. Chem..

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

[4]  Kuo-Chen Chou,et al.  The convergence‐divergence duality in lectin domains of selectin family and its implications , 1995, FEBS letters.

[5]  Mark Gerstein,et al.  Sequences and topology. , 2001, Current opinion in structural biology.

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

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

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

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

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

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

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

[13]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

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

[15]  Thomas L. Madden,et al.  Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. , 2001, Nucleic acids research.

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

[17]  D. Barrell,et al.  The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. , 2003, Genome research.

[18]  Mark Gerstein,et al.  Editorial overviewSequences and topology , 2003 .

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

[20]  K. Chou.  Structural bioinformatics and its impact to biomedical science. , 2004, Current medicinal chemistry.

[21]  Kimberly Van Auken,et al.  WormBase: a multi-species resource for nematode biology and genomics , 2004, Nucleic Acids Res..

[22]  Martin Ester,et al.  Sequence analysis PSORTb v . 2 . 0 : Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis , 2004 .

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

[24]  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.

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

[26]  Loris Nanni,et al.  Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization , 2008, Amino Acids.

[27]  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.

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

[29]  R. Murphy,et al.  Automated subcellular location determination and high-throughput microscopy. , 2007, Developmental cell.

[30]  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.

[31]  Shao-Wu Zhang,et al.  Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies , 2008, Amino Acids.

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

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

[34]  Shao-Wu Zhang,et al.  Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach , 2008, Amino Acids.

[35]  K. Chou,et al.  PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. , 2008, Analytical biochemistry.

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

[37]  Renato R. O. da Silva,et al.  Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques , 2009, BSB.

[38]  C. Orengo,et al.  Protein function annotation by homology-based inference , 2009, Genome Biology.

[39]  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.

[40]  Rachael P. Huntley,et al.  The GOA database in 2009—an integrated Gene Ontology Annotation resource , 2008, Nucleic Acids Res..

[41]  A. Millar,et al.  Exploring the Function-Location Nexus: Using Multiple Lines of Evidence in Defining the Subcellular Location of Plant Proteins , 2009, The Plant Cell Online.

[42]  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.

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

[44]  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.

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

[46]  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.

[47]  K. Chou,et al.  Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. , 2010, Journal of theoretical biology.

[48]  Ying Gao,et al.  Bioinformatics Applications Note Sequence Analysis Cd-hit Suite: a Web Server for Clustering and Comparing Biological Sequences , 2022 .

[49]  Ganapati Panda,et al.  A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction , 2010, Comput. Biol. Chem..

[50]  Michelle S. Scott,et al.  PNAC: a protein nucleolar association classifier , 2011, BMC Genomics.

[51]  Hassan Mohabatkar,et al.  Prediction of cyclin proteins using Chou's pseudo amino acid composition. , 2010, Protein and peptide letters.

[52]  K. Chou,et al.  iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins , 2011, PloS one.

[53]  K. Chou,et al.  iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model , 2011, PloS one.

[54]  Kuo-Chen Chou,et al.  A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites , 2011, PloS one.

[55]  H. Mohabatkar,et al.  Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach , 2011, Journal of Structural and Functional Genomics.

[56]  Loris Nanni,et al.  Wavelet images and Chou’s pseudo amino acid composition for protein classification , 2011, Amino Acids.

[57]  Qiang Yang,et al.  Multitask Learning for Protein Subcellular Location Prediction , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[58]  K. Chou.  Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.

[59]  Jianxiu Guo,et al.  Predicting protein folding rates using the concept of Chou's pseudo amino acid composition , 2011, Journal of computational chemistry.

[60]  K. Chou,et al.  iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. , 2011, Journal of theoretical biology.

[61]  Dongsheng Zou,et al.  Supersecondary structure prediction using Chou's pseudo amino acid composition , 2011, J. Comput. Chem..

[62]  A. Esmaeili,et al.  Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. , 2011, Journal of theoretical biology.

[63]  K. Chou,et al.  iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. , 2011, Molecular bioSystems.

[64]  Xiao-hui Niu,et al.  Predicting protein solubility by the general form of Chou's pseudo amino acid composition: approached from chaos game representation and fractal dimension. , 2012, Protein and peptide letters.

[65]  K. Chou,et al.  iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. , 2012, Molecular bioSystems.

[66]  Suyu Mei,et al.  Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization. , 2012, Journal of theoretical biology.

[67]  Suyu Mei,et al.  Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning. , 2012, Journal of theoretical biology.

[68]  Samad Jahandideh,et al.  Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection. , 2012, Journal of theoretical biology.

[69]  Wei Chen,et al.  iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties , 2012, PloS one.

[70]  Wenqi Liu,et al.  Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites , 2012, PloS one.

[71]  Zia-ur-Rehman,et al.  Identifying GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix. , 2012, Protein and peptide letters.

[72]  Changqing Li,et al.  An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity , 2012, PloS one.

[73]  Asifullah Khan,et al.  MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM. , 2012, Journal of theoretical biology.

[74]  Dinesh Gupta,et al.  Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou's Pseudo Amino Acid Composition and on Evolutionary Information , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[75]  Shao-Ping Shi,et al.  Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. , 2012, Molecular bioSystems.

[76]  Jeff G. Schneider,et al.  Protein subcellular location pattern classification in cellular images using latent discriminative models , 2012, Bioinform..

[77]  Maqsood Hayat,et al.  Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou's PseAAC. , 2012, Protein and peptide letters.

[78]  K. Chou,et al.  iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins. , 2012, Protein and peptide letters.

[79]  Xin Wang,et al.  PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. , 2012, Analytical biochemistry.

[80]  K. Chou,et al.  Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model , 2012, PloS one.

[81]  Hassan Mohabatkar,et al.  Prediction of allergenic proteins by means of the concept of Chou's pseudo amino acid composition and a machine learning approach. , 2012, Medicinal chemistry (Shariqah (United Arab Emirates)).

[82]  Wei Chen,et al.  iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition , 2013, Nucleic acids research.

[83]  Kuo-Bin Li,et al.  Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition. , 2013, Journal of theoretical biology.