Abstract
A novel approach of glide zoom window feature extraction based on protein sequence was proposed for predicting protein homo-oligomers. Based on the concept of glide zoom window, three feature vectors of amino acids distance sum, amino acids mean distance and amino acids distribution, were extracted. A series of feature sets were constructed by combing these feature vectors with amino acids composition to form pseudo amino acid compositions (PseAAC). The support vector machine (SVM) was used as base classifier. The 73.19% total accuracy is arrived in jackknife test, which is 7.87% higher than that of conventional amino acid composition method. The results show that the novel feature extraction method of glide zoom window is effective and feasible, and the feature vectors extracted with this method may contain more protein structure information.
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References
Chou, K.C.: Review: Low-frequency Collective Motion in Biomacromolecules and Its Biological Functions. Biophys. Chem. 30, 3–48 (1988)
Chou, K.C.: Review: Structural Bioinformatics and Its Impact to Biomedical Science. Curr. Med. Chem. 11, 2105–2134 (2004e)
Chou, K.C.: Molecular Therapeutic Rarget for Type-2 Diabetes. J. Proteome Res. 3, 1284–1288 (2004a)
Chou, K.C.: Insights from Modeling Three-dimensional Structures of the Human Potassium and Sodium Channels. J. Proteome Res. 3, 856–861 (2004)
Chou, K.C.: Insights from Modeling the 3D Structure of the Extracellular Domain of Alpha7 Nicotinic Acetylcholine Receptor. Biochem. Biophys. Res. Commun. 319, 433–438 (2004)
Chou, K.C.: Modelling Extracellular Domains of GABA-A Receptors: Subtypes 1, 2, 3, and 5. Biochem. Biophys. Res. Commun. 316, 636–642 (2004)
Oxenoid, K., Chou, J.J.: The Structure of Phospholamban Pentamer Reveals a Channel-like Architecture in Membranes. Proc. Natl. Acad. Sci. USA 102, 10870–10875 (2005)
Anfinsen, C.B., Haber, E., Sela, M., White, F.H.: The Kinetics of the Formation of Native Ribonuclease During Oxidation of the Reduced Polypeptide Chain. Proc. Natl. Acad. Sci. USA 47, 1309–1314 (1961)
Anfisen, C.B.: Principles That Govern the Folding of Protein Chains. Science 181, 223–230 (1973)
Jones, S., Thornton, J.M.: Analysis of Protein–protein Interaction Sites Using Surface Patches. J. Mol. Biol. 272, 121–132 (1997a)
Jones, S., Thornton, J.M.: Prediction of Protein–protein Interaction Sites Using Patch Analysis. J. Mol. Biol. 272, 133–143 (1997b)
Garian, R.: Prediction of Quaternary Structure from Primary Structure. Bioinformatics 17, 551–556 (2001)
Chou, K.C., Cai, Y.D.: Predicting Protein Quaternary Structure by Pseudo Amino Acid Composition. Proteins Struct. Func. Gene. 53, 282–289 (2003b)
Zhang, S.W., Quan, P., Zhang, H.C., Zhang, Y.L., Wang, H.Y.: Classification of Protein Quaternary Structure with Support Vector Machine. Bioinformatics 19, 2390–2396 (2003)
Zhang, S.W., Pan, Q., Zhang, H.C., Shao, Z.C., Shi, J.Y.: Prediction of Protein Homo-oligomer Types by Pseudo Amino Acid Composition: Approached with an Improved Feature Extraction and Naive Bayes Feature Fusion Amino Acids (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Bairoch, A., Apweiler, R.: The SWISS-PROT Protein Data Bank and Its New Supplement TrEMBL. Nucleic Acids Res. 24, 21–25 (1996)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions in Neural Networks 13(2), 415–425 (2002)
Kreßel, U.H.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, C.J., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning: 1999, pp. 255–268. MIT Press, Cambridge (1999)
Ding, C.H., Dubchak, I.: Multi-class Protein Fold Recognition Using Support Vector Machines and Neural Networks. Bioinformatics 17(4), 349–358 (2001)
Platt, J., Cristianini, N., Shawe-Taylor, J.: Large Margin Dags for Multiclass Classification. In: Jordan, M.I., Lecun, Y., Solla, S. (eds.) Proceedings of Neural Information Processing Systems, pp. 547–553. MIT Press, Cambridge (2000)
Chou, K.C., Zhang, C.T.: Review: Prediction of Protein Structural Classes. Crit. Rev. Biochem. Mol. Biol. 30, 275–349 (1995)
Zhou, G.P.: Assa-Munt N Some Insights Into Protein Structural Class Prediction. Proteins Struct. Funct. Genet. 44, 57–59 (2001)
Fasman, G.D.: Handbook of Biochemistry and Molecular Biology, 3rd edn. CRC Press, Boca Raton (1976)
Bahar, I., Atilgan, A.R., Jernigan, R.L., Erman, B.: Understanding the Recognition of Protein Structural Classes by Amino Acid Composition. Proteins 29, 172–185 (1997)
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Li, QP., Zhang, SW., Pan, Q. (2008). Prediction of Protein Homo-oligomer Types with a Novel Approach of Glide Zoom Window Feature Extraction. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_10
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DOI: https://doi.org/10.1007/978-3-540-87442-3_10
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