Abstract
Given a membrane protein sequence, how can we identify its type, particularly when a query protein may have the multiplex character, i.e., simultaneously exist at two or more different types. However, most of the existing predictors or methods can only be used to deal with the single-type or “singleplex” membrane proteins. Actually, multiple-type or “multiplex” membrane proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. In this study, three different models were developed, which have the ability to deal with the systems containing both singleplex and multiplex membrane proteins. The overall success rate thus obtained was 0.6440, indicating that the study may become a very useful high-throughput tool in identifying the functional types of membrane proteins.
Similar content being viewed by others
References
Cai Y-D, Zhou G-P, Chou K-C (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 84:3257–3263
Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43:246–255
Chou K-C (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19
Chou K-C (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273:236–247
Chou K-C, Cai Y-D (2005) Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model 45:407–413
Chou K-C, Shen H-B (2007) MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun. 360:339–345
Consortium, U (2008) The universal protein resource (UniProt). Nucleic Acids Res 36:D190–D195
Feng Z-P, Zhang C-T (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem 19:269–275
Hayat M, Khan A (2011) Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J Theor Biol 271:10–17
Hayat M, Khan A (2012) MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM. J Theor Biol 292:93–102
Huang C, Yuan J-Q (2013a) A multilabel model based on Chou’s pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types. J Membr Biol 246:327–334
Huang C, Yuan J-Q (2013b) Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J Theor Biol 335:205–212
Huang Y, Niu B, Gao Y, Fu L, Li W (2010) CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26:680–682
Huang G, Zhang Y, Chen L, Zhang N, Huang T, Cai Y-D (2014) Prediction of multi-type membrane proteins in human by an integrated approach. PLoS ONE 9:e93553
Jian X, Wei R, Zhan T, Gu Q (2008) Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept Lett 15:392–396
Lin H (2008) The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J Theor Biol 252:350–356
Lin W-Z, Fang J-A, Xiao X, Chou K-C (2013) iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol BioSyst 9:634–644
Mohabatkar H (2010) Prediction of cyclin proteins using Chou’s pseudo amino acid composition. Protein Pept Lett 17:1207–1214
Nanni L, Brahnam S, Lumini A (2012) Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids 43:657–665
Pu X, Guo J, Leung H, Lin Y (2007) Prediction of membrane protein types from sequences and position-specific scoring matrices. J Theor Biol 247:259–265
Qiu J-D, Sun X-Y, Huang J-H, Liang R-P (2010) Prediction of the types of membrane proteins based on discrete wavelet transform and support vector machines. Protein J 29:114–119
Schäffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 29:2994–3005
Wu Z-C, Xiao X, Chou K-C (2012) iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex gram-positive bacterial proteins. Protein Pept Lett 19:4–14
Xiao X, Shao S, Ding Y, Huang Z, Chou K-C (2006) Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30:49–54
Xiao X, Wang P, Lin W-Z, Jia J-H, Chou K-C (2013) iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436:168–177
Zeng Y-H, Guo Y-Z, Xiao R-Q, Yang L, Yu L-Z, Li M-L (2009) Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 259:366–372
Zhang M-L, Zhou Z-H (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40:2038–2048
Zou H-L (2014) A multi-label classifier for prediction membrane protein functional types in animal. J Membr Biol 247:1141–1148
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Zou, HL., Xiao, X. Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou’s Pseudo Amino Acid Compositions. J Membrane Biol 249, 23–29 (2016). https://doi.org/10.1007/s00232-015-9830-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00232-015-9830-9