Abstract
Predicting the tertiary structure of protein from its primary amino acid sequence is a challenging mission for bioinformatics. In this paper we proposes a novel approach of predicting the tertiary structure of protein using the flexible neural tree (FNT) to construct a tree classification model. Two feature extraction methods (the physicochemical composition (PCC)) and the recurrence quantification analysis (RQA)) are employed to extract the features of protein sequence. To value the efficiencies of the proposed method we select two benchmark protein sequence datasets (1189 dataset and 640 dataset), as the test data set. The experimental results show that the proposed method is efficient for the 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)
Kumar, M., Bhasin, M., Natt, N.K., Raghava, G.P.S.: BhairPred: Prediction of B-hairpins in A PROtein From Multiple Alignment Information using ANN and SVM techniques. Nucleic Acids Res. 33, 154–159 (2005)
Metfessel, B.A., Saurugger, P.N., Connelly, D.P., Rich, S.T.: Cross-validation of Protein Structural Class Prediction using Statistical clustering and neural networks. Protein Sci. 1171–1182 (1993)
Cai, Y.D., et al.: Prediction of Protein Structural Classes by Support Vector Machines. Computers & Chemistry 26(3), 293–296 (2002)
Chen, Y.H., Abraham, A., Yang, J.: Feature Selection and Intrusion Detection Using Hybrid Flexible Neural Tree, pp. 439–444. Springer, Heidelberg (2005)
Salustowiczand, R., Schmidhuber, J.: Probabilistic Incremental Program Evolution. Evolutionary Computation 5(2), 123–141 (1997)
Wang, Z.X., Yuan, Z.: How good is The Prediction of Protein Structural Class by The Component-coupled Method. Proteins 38, 165–175 (2000)
Chen, K., Kurgan, L.A., Ruan, J.: Prediction of Protein Structural Class using Novel Evolutionary Collocation-based Sequence Representation. J. Comput. Chem. 29, 1596–1604 (2008)
Chen, C., Chen, L.X., et al.: Predicting protein Structural Class Based on Multi-features Fusion. Journal of Theoretical Biology 253, 388–392 (2008)
Yang, J.Y., Deogun, J.: Classifying G Protein-Coupled Receptors with Multiple Physicochemical Properties. Chem-Bio Informatics Journal 8, 49–57 (2008)
Giuliani, A., Sirabella, P., Benigni, R., Colosimo, A.: Mapping Protein Sequence Spaces by Recurrence: A Case Study On Chimeric Structures. Protein Eng. 13, 671–678 (2000)
Yang, J.Y., Peng, Z.L., et al.: Prediction of Protein Structural Classes by Recurrence Quantification Analysis Based on Chaos Game Representation. J. Theor. Biol. 257(4), 618–626 (2009)
Wang, Z.X., Yuan, Z.: How Good Is The Prediction of Protein Structural Class by The Component-coupled Method? Pattern Recogn. 38, 165–175 (2000)
Kurgan, L.A., Homaeian, L.: Prediction of Structural Classes for Protein Sequences and Domains-Impact of Prediction Algorithms, Sequence Representation and Homology, and Test Procedures on Accuracy. Pattern Recogn. 39, 2323–2343 (2006)
Kedarisetti, K.D., Kurgan, L.A., Dick, S.: Classifier Ensembles for Protein Structural Class Prediction with Varying Homology. Biochem. Biophys. Res. Commun. 348, 981–988 (2006)
Zhang, T.L., Ding, Y.S., Chou, K.C.: Prediction Protein Structural Classes with Pseudo-amino Acid Composition: Approximate Entropy and Hydrophobicity Pattern. J. Theor. Biol. 250, 186–193 (2008)
Chen, K., Kurgan, A., Ruan, J.S.: Prediction of Protein Structural Class using Novel Evolutionary Collocation-Based Sequence Representation. J. Computational Chemistry 29, 1596–1604 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shao, G., Chen, Y. (2012). Predict the Tertiary Structure of Protein with Flexible Neural Tree. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-31576-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31575-6
Online ISBN: 978-3-642-31576-3
eBook Packages: Computer ScienceComputer Science (R0)