Abstract
Protein structure prediction is an important area of research in bioinformatics. In this research, a novel method to predict the structure of the protein is introduced. The amino acid frequencies, generalization dipeptide composition and typical hydrophobic composition of protein structure are treated as candidate feature. Flexible neural tree and neural network are employed as classification model. To evaluate the efficiency of the proposed method, a classical protein sequence dataset (1189) is selected as the test dataset. The results show that the method is efficient for protein structure prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Levitt, M., Chothia, C.: Structural patterns in globular proteins. Nature 261, 552–558 (1976)
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)
Xu Y&Xu, D.: Protein threading using PROSPECT: Design and evaluation. Proteins: Structure, Function, and Genetics 40, 343–354 (2000)
Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5, 823–826 (1986)
Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5(4), 823–826 (1986)
Chung, S.Y., Subbiah, S.: A structural explanation for the twilight zone of protein sequence homology. Structure 4, 1123–1127 (1996)
Kaczanowski, S., Zielenkiewicz, P.: Why similar protein sequences encode similar three-dimensional structures. Theoretical Chemistry Accounts 125, 543–550 (2010)
Bairoch, A., Apweiler, R., Wu, C.H., et al.: The Universal Protein Resource (UniProt). Nucleic Acids Res. 33(Database Issue), D154–D159 (2005)
Helliwell, J.R.: Protein crystal perfection and its application. Acta Crystallographica D61(pt. 6): 793–798 (2005)
Nassif, H., Al-Ali, H., Khuri, S., Keirouz, W.: Prediction of protein-glucose binding sites using support vector machines. Proteins 77(1), 121–132 (2009)
Söding, J.: Protein homology detection by HMM-HMM comparison. Bioinformatics 21(7), 951–960 (2005)
Wang, Y., Wu, L.Y., Zhang, X.S., Chen, L.: Automatic classification of protein structures based on convex hull representation by integrated neural network. In: Cai, J.-Y., Cooper, S.B., Li, A. (eds.) TAMC 2006. LNCS, vol. 3959, pp. 505–514. Springer, Heidelberg (2006)
Zhang, X.S., Zhan, Z.W., Wang, Y., Wu, L.Y.: An Attempt to Explore the Similarity of Two Proteins by Their Surface Shapes. In: Operations Research and Its Applications. Lecture Notes in Operations Research, vol. 5, pp. 276–284. World Publishing Corporation, Beijing (2005)
Kohonen, J., Talikota, S., Corander, J., Auvinen, P., Arjas, E.: A Naive Bayes classifier for protein function prediction. Silico. Biol. 9(1-2), 23–34 (2009)
Wang, Z.-X., Yuan, Z.: How good is the prediction of protein structural class by the component-coupled method? Proteins 38, 165–175 (2000)
Zhang, C.T., Chou, K.C.: An optimization approach to predicting protein structural class from amino-acid composition. Protein Science 1, 401–408 (1992)
Zhang, C.T., Chou, K.C., Maggiora, G.M.: Predicting protein structural classes from amino acid composition: application of fuzzy clustering. Protein Engineering 8, 425–435 (1995)
Kedarisetti, K., Kurgan, L., Dick, S.: A comment on Prediction of protein structural classes by a new measure of information discrepancy (2006) (accepted)
Kedarisetti, K.D., Kurgan, L., Dick, S.: Classifier ensembles for protein structural class prediction with varying homology. Biochemical and Biophysical Research Communications 348, 981–988 (2006)
Liu, T., Jia, C.: A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. J. Theor. Biol. 267, 272–275 (2010)
Shao, G., Chen, Y.: Predict the Tertiary Structure of Protein with Flexible Neural Tree. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS, vol. 7390, pp. 324–331. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bao, W., Chen, Y., Wang, D., kong, F., Yu, G. (2014). Prediction of Protein Structure Classes with Ensemble Classifiers. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)