Abstract
Data mining methods are helpful in analyzing large amount of sequence and structure information of proteins. Classifiers can do a good job in achieving accurate mechanism classification of glycoside hydrolases which have different physicochemical properties. This classification method is not limited by reaction conditions. In this paper, a new method is proposed to classify the catalytic mechanism of a certain glycoside hydrolase according to their sequence and structure features by using several classifiers. Through making a comparison of the classification results achieved by the k-nearest neighbor (kNN) classifier and the Naive Bayes (NB) classifier and Multilayer Perceptron (MLP)classifier, the kNN classifier is approved to be an ideal choice in classifying and predicting the catalytic mechanisms of glycoside hydrolases with various physicochemical properties.
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References
Henrissat, B., Davies, G.: Structural and sequence-based classification of glycoside hydrolases. Current Opinion in Structural Biology 7, 637–644 (1997)
Yang, J.K., Yoon, H.J., Ahn, H.J., Il Lee, B., Pedelacq, J., Liong, E.C., Berendzen, J., Laivenieks, M., Vieille, C., Zeikus, G.J.: Crystal Structure of [beta]-d-Xylosidase from Thermoanaerobacterium saccharolyticum, a Family 39 Glycoside Hydrolase. Journal of Molecular Biology 335, 155–165 (2004)
Uversky, V.N., Wohlkönig, A., Huet, J., Looze, Y., Wintjens, R.: Structural Relationships in the Lysozyme Superfamily: Significant Evidence for Glycoside Hydrolase Signature Motifs. PLoS ONE 5, e15388 (2010)
Honda, Y., Fushinobu, S., Hidaka, M., Wakagi, T., Shoun, H., Taniguchi, H., Kitaoka, M.: Alternative strategy for converting an inverting glycoside hydrolase into a glycosynthase. Glycobiology 18, 325 (2008)
Borro, L.C., Oliveira, S.R.M., Yamagishi, M.E.B., Mancini, A.L., Jardine, J.G., Mazoni, I., dos Santos, E.H., Higa, R.H., Kuser, P.R., Neshich, G.: Predicting enzyme class from protein structure using Bayesian classification. Genet. Mol. Res. 5, 193–202 (2006)
Lee, B.J., Lee, H.G., Lee, J.Y., Ryu, K.H.: Classification of Enzyme Function from protein sequence based on feature representation. In: IEEE International Conference on Bioinformatics and Bioengineering - BIBE, pp. 741–747. IEEE Press, Boston (2007)
Nigsch, F., Bender, A., van Buuren, B., Tissen, J., Nigsch, E., Mitchell, J.B.O.: Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. Journal of Chemical Information and Modeling 46, 2412–2422 (2006)
Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M., Appel, R.D., Bairoch, A.: Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook, pp. 571–607 (2005)
Nasibov, E.N., Kandemir-Cavas, C.: Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction. Computational Biology and Chemistry 33, 461–464 (2009)
Valavanis, I.K., Spyrou, G.M., Nikita, K.S.: A comparative study of multi-classification methods for protein fold recognition. International Journal of Computational Intelligence in Bioinformatics and Systems Biology 1, 332–346 (2010)
Towfic, F., Caragea, C., Dobbs, D., Honavar, V.: Struct-NB: predicting protein-RNA binding sites using structural features. International Journal of Data Mining and Bioinformatics 4, 21–43 (2010)
Nanni, L., Lumini, A.: A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease. Protein and Peptide Letters 16, 163–167 (2009)
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Yang, F., Wang, L. (2011). Research on Classification Methods of Glycoside Hydrolases Mechanism. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_73
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DOI: https://doi.org/10.1007/978-3-642-24955-6_73
Publisher Name: Springer, Berlin, Heidelberg
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