Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine

  • Jianyu Shi
  • Shaowu Zhang
  • Yan Liang
  • Quan Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)


As more and more genomes have been discovered in recent years, it is an urgent need to develop a reliable method to predict protein subcellular localization for further function exploration. However many well-known prediction methods based on amino acid composition, have no ability to utilize the information of sequence-order. Here we propose a novel method, named moment descriptor (MD), which can obtain sequence order information in protein sequence without the need of the information of physicochemical properties of amino acids. The presented method first constructs three types of moment descriptors, and then applies multi-class SVM to the Chou’s dataset. Through resubstitution, jackknife and independent tests, it is shown that the MD is better than other methods based on various types of extensions of amino acid compositions. Moreover, three multi-class SVMs show similar performance except for the training time.


Support Vector Machine Amino Acid Composition Directed Acyclic Graph Feature Extraction Method Protein Subcellular Localization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Feng, Z.P.: An Overview on Predicting Subcellular Location of a Protein. Silico. Biol. 2, 27 (2002)Google Scholar
  2. 2.
    Nakashima, H., Nishikawa, K.: Discrimination of Intracellular and Extracellular Proteins Using Amino Acid Composition and Residue-Pair Frequencies. J. Mol. Biol. 238, 54–61 (1994)CrossRefGoogle Scholar
  3. 3.
    Feng, Z.P., Zhang, C.T.: Prediction of the Subcellular Localization of Prokaryotic Proteins Based on the Hydrophobicity Index of Amino Acids. Int. J. Biol. Macromol. 28, 255–261 (2001)CrossRefGoogle Scholar
  4. 4.
    Feng, Z.P., Zhang, C.T.: A Graphic Representation of Protein Sequence and Predicting the Subcellular Localizations of Prokaryotic Proteins. J. Biochem. Cell Biol. 34, 298–307 (2002)CrossRefGoogle Scholar
  5. 5.
    Chou, K.C.: Prediction of Protein Cellular Attributes Using Pseudo – Amino – Acid –Composition. Proteins 43, 246–255 (2001)CrossRefGoogle Scholar
  6. 6.
    Zhou, G.P., Doctor, K.: Subcellular Location Prediction of Apoptosis Proteins. Proteins 50, 44–48 (2003)CrossRefGoogle Scholar
  7. 7.
    Cai, Y.D., Chou, K.C.: Nearest Neighbour Algorithm for Predicting Protein Subcellular by Combining Functional Domain Composition and Pseudo Amino Acid Composition. Biochem. Biophys. Res. Commun. 305, 407–411 (2003)CrossRefGoogle Scholar
  8. 8.
    Chou, K.C., Cai, Y.D.: A New Hybrid Approach to Predict Subcellular Localization of Proteins by Incorporating Gene Ontology. Biochem. Biophys. Res. Commun. 311, 743–747 (2003)CrossRefGoogle Scholar
  9. 9.
    Chou, K.C., Cai, Y.D.: Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location. J. Biol. Chem. 277, 45765–45769 (2002)CrossRefGoogle Scholar
  10. 10.
    Pan, Y.X., Zhang, Z.Z., Guo, Z.M., Feng, G.Y., Huang, Z.D., He, L.: Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach. J. Protein Chem. 22, 395–402 (2003)CrossRefGoogle Scholar
  11. 11.
    Bhasin, M., Raghava, G.P.S.: ESLpred: SVM-Based Method for Subcellular Localization of Eukaryotic Proteins Using Dipeptide Composition and PSI-BLAST. Nucleic Acids Res. 32, W414–W419 (2004)CrossRefGoogle Scholar
  12. 12.
    Park, K.J., Kanehisa, M.: Prediction of Protein Subcellular Locations by Support Vector Machines Using Compositions of Amino Acids and Amino Acid Pairs. Bioinformatics 19, 1656–1663 (2003)CrossRefGoogle Scholar
  13. 13.
    Cui, Q., Jiang, T., Liu, B., Ma, S.: Esub8: A Novel Tool to Predict Protein Subcellular Localizations in Eukaryotic Organisms. BMC Bioinformatics 5, 66–72 (2004)CrossRefGoogle Scholar
  14. 14.
    Chou, K.C.: A Novel Approach to Predicting Protein Structural Classes in a (20-1)-D Amino Acid Composition Space. Proteins 21, 319–344 (1995)CrossRefGoogle Scholar
  15. 15.
    Reinhardt, A., Hubbard, T.: Using Neural Networks for Prediction of the Subcellular Localization of Proteins. Nucleic Acids Res. 26, 2230–2236 (1998)CrossRefGoogle Scholar
  16. 16.
    Chou, K.C., Elrod, D.: Protein Subcellular Localization Prediction. Protein Eng. 12, 107–118 (1999)CrossRefGoogle Scholar
  17. 17.
    Yuan, Z.: Prediction of protein subcellular localizations using Markov chain models. FEBS Lett. 451, 23–26 (1999)CrossRefGoogle Scholar
  18. 18.
    Huang, Y., Li, Y.D.: Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics 20, 21–28 (2001)CrossRefGoogle Scholar
  19. 19.
    Hua, S.J., Sun, Z.R.: Support Vector Machine Approach for Protein Subcellular Localization Prediction. Bioinformatics 17, 721–728 (2001)CrossRefGoogle Scholar
  20. 20.
    Zhang, S.W., Pan, Q., Zhang, H.C., Shao, Z.C., Shi, J.Y.: Prediction Protein Homo-oligomer Types by Pesudo Amino Acid Composition: Approached with an Improved Feature Extraction and Naive Bayes Feature Fusion, Amino Acid (in press, 2006)Google Scholar
  21. 21.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  22. 22.
    Bredensteiner, E., Bennet, K.: Multicategory Classification by Support Vector Machines. Comput. Optim. Appl. 12, 53–79 (1999)CrossRefMathSciNetzbMATHGoogle Scholar
  23. 23.
    Crammer, K., Singer, Y.: On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines. J. Mach. Learn. Res. 2, 265–292 (2001)CrossRefGoogle Scholar
  24. 24.
    Kreßel, U.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, C.J., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learnings, pp. 255–268. MIT Press, Cambridge (1999)Google Scholar
  25. 25.
    Platt, J., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 547–553 (2000)Google Scholar
  26. 26.
    Hsu, C., Lin, C.J.: A Comparison of Methods for Multi-Class Support Vector Machines. IEEE. T. Neural Networks 13, 415–425 (2002)CrossRefGoogle Scholar
  27. 27.
    Rifin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianyu Shi
    • 1
  • Shaowu Zhang
    • 1
  • Yan Liang
    • 1
  • Quan Pan
    • 1
  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina

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