Abstract
Prediction of protein structural classes is an important area in bioinformatics, it is beneficial to research protein function, regulation and interactions. In this paper, a 20-dimensional feature vector is extracted based on the predicted secondary structure sequence and the corresponding E-H sequence. Hierarchical classification model based on flexible neural tree (FNT) which is a special kind of artificial neural network with flexible tree structures is used to complete the experiment. 640 dataset and 25 pdb dataset with low homology are chosen as the test dataset. The 10-fold cross validation test is used to test and compare this method with other existing methods. The overall accuracies of our method are 2.7 % and 3.2 % higher for the two datasets respectively.
These authors contributed equally to this work and should be considered co-first authors.
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
References
Chou, K.C.: Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem. 11, 2105–2134 (2004)
Chou, K.C., Wei, D.Q., Du, Q.S., Sirois, S., Zhong, W.Z.: Progress in computational approach to drug development against SARS. Curr. Med. Chem. 13, 3263–3270 (2006)
Levitt, M., Chothia, C.: Structural patterns in globular proteins. Nature 261, 552–558 (1976)
Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: A structural classification of protein database for the investigation of sequence and structures. J. Mol. Biol. 247, 536–540 (1995)
Yang, J., Peng, Z., Chen, X.: Prediction of protein structural classes for low-homology sequences based on predicted secondary structure. BMC Bioinform. 11, S9 (2010)
Kurgan, L., Cios, K., Chen, K.: SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinform. 9, 226 (2008)
Mizianty, M.J., Kurgan, L.: Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences. BMC Bioinform. 10, 414 (2009)
Liu, T., Jia, C.: A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. J. Theor. Biol. 267, 272–275 (2010)
Yang, J.Y., Peng, Z.L., Yu, Z.G., Zhang, R.J., Anh, V., Wang, D.S.: Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. J. Theor. Biol. 257, 618–626 (2009)
Ding, S., Zhang, S., Li, Y., Wang, T.: A novel protein structural classes prediction method based on predicted secondary structure. Biochimie 94, 1166–1171 (2012)
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)
Wang, Z.: The current situation and prospect of protein structure prediction. Chem. Life 18(6), 19–22 (1998)
Jones, D.T.: Protein secondary structure prediction based on position specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174, 219–235 (2005)
Yang, B., Chen, Y., Jiang, M.: Reverse engineering of gene regulatory networks using flexible neural tree models. Neurocomputing 99, 458–466 (2013)
Zhao, G., et al.: Bioinformatics, pp. 158–160. Science Press, Beijing (2002)
Tan, A.C., Gilbert, D.: Machine learning learning and its application to bioinformatics: an overview [M]. 31 Aug. 2001
He, Y., Chen, Z., Yang, Y.: Research on a hierarchical text categorization method based on centroid. J. Inf. Technol. 12, 116–118 (2007)
Hsu, C.W., Lin, C.J.: A comparison of methods for multi-class support vector machines. J. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Ma, X., Huang, X., Chai, Y.: 2PTMC classification algorithm based on support vector machines and its application to fault diagnosis. J. Control Decis. 18(3), 272–276 (2003)
An, J., Wang, Z., Ma, Z.: A new SVM multiclass classification method. J. Inf. Control 33(3), 262–267 (2004)
Lu, Y., Lu, J., Yang, J.: A survey of hierarchical classification methods. J. Patt. Recog. Artif. Intell. 26(12), 1131–1139 (2013)
Acknowledgements
Fanliang Kong and Dong Wang contributed equally to this work and should be considered co-first authors. This research was partially supported by Program for Scientific research innovation team in Colleges and universities of Shandong Province 2012–2015, the Key Project of Natural Science Foundation of Shandong Province (ZR2011FZ001), the Natural Science Foundation of Shandong Province (ZR2011FL022, ZR2013FL002), the Key Subject Research Foundation of Shandong Province and the Shandong Provincial Key Laboratory of Network Based Intelligent Computing. This work was also supported by the National Natural Science Foundation of China (Grant No. 61302128, 61201428, 61203105). The scientific research foundation of University of Jinan (xky1109).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kong, F., Wang, D., Bao, W., Chen, Y. (2015). Prediction of Protein Structural Classes Based on Predicted Secondary Structure. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-22186-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22185-4
Online ISBN: 978-3-319-22186-1
eBook Packages: Computer ScienceComputer Science (R0)