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A Novel Multi-Label Predictor for Identifying Multi-Functional Classes of Human Membrane Proteins

Volume 14, Number 7, July 2018, pp. 1628-1634
DOI: 10.23940/ijpe.18.07.p27.16281634

Xiao Wang, Guoqing Li, Weiwei Zhang, Hongwei Tao, and Yinghui Meng

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

(Submitted on April 4, 2018; Revised on May 17, 2018; Accepted on June 23, 2018)


Knowing which types of functionality that human membrane proteins belong to is very helpful for understanding their functions. However, most existing online prediction methods have some disadvantages, including: 1) they obtain very low prediction accuracy, and 2) they can only predict single-functional classes of cytomembrane proteins in humans. To overcome the drawbacks, a new multi-label predictor, namely mMem-Hum, is proposed. In addition to predicting types of single-function membrane proteins, it can also predict multi-functional types. Specifically, discriminative features of membrane proteins are generated by using amino acid sequence information and evolutionary information, and then they are classified by a new multi-label classifier that utilizes label correlations. Experimental results reveal that the performance of mMem-Hum is significantly better than other existing forecasting methods. This indicates that mMem-Hum may become a promising prediction tool for classifying functional classes of cytomembrane proteins in humans.


References: 10

          1. M. S. Almén, K. J. V. Nordström, R. Fredriksson, and H. B. Schiöth, “Mapping the human membrane proteome: a majority of the human membrane proteins can be classified according to function and evolutionary origin,” BMC Biology, vol. 7, article 50, 2009
          2. K.-C. Chou, “Prediction of Protein Cellular Attributes Using Pseudo-Amino Acid Composition,” Proteins: Structure, Function, and Bioinformatics, vol. 43, no. 3, pp. 246-255, 2001
          3. K. C. Chou and H. B. Shen, “MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM,” Biochemical and Biophysical Research Communications, vol. 360, no. 2, pp. 339-345, 2007
          4. Y. D. Cai, G. P. Zhou, and K. C. Chou, “Support vector machines for predicting membrane protein types by using functional domain composition,” Biophysical Journal, vol. 84, no. 5, pp. 3257-3263, 2003
          5. C. Ding, L. F. Yuan, S. H. Guo, H. Lin, and W. Chen, “Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions,” Journal of Proteomics, vol. 77, no. 24, pp. 321-328, 2012
          6. C. Huang and J.-Q. Yuan, “A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types,” Journal of Membrane Biology, vol. 246, no. 4, pp. 327-334, 2013
          7. L. Nanni and A. Lumini, “An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence,” Amino Acids, vol. 35, no. 3, pp. 573-580, 2008.
          8. C.-T. Su, C.-Y. Chen, and Y.-Y. Ou, “Protein Disorder Prediction by Condensed PSSM Considering Propensity for Order or Disorder,” BMC Bioinformatics, vol. 7, article 319, 2006.
          9. S. Wan, M.-W. Mak, and S.-Y. Kung, “Mem-mEN: predicting multi-functional types of membrane proteins by interpretable elastic nets,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 706-718, 2016.
          10. H. L. Zou and X. Xiao, “A New Multi-label Classifier in Identifying the Functional Types of Human Membrane Proteins,” Journal of Membrane Biology, vol. 248, no. 2, pp. 179-186, 2015.


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