Prediction of enzyme classes in a hierarchical approach by using spmap

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2009

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Yaman, Ayşe Gül

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Enzymes are proteins that play an important role in biochemical reactions as catalysts. They are classified based on the reaction they catalyzed, in a hierarchical scheme by International Enzyme Commission (EC). This hierarchical scheme is expressed as a four-level tree structure and a unique number is assigned to each enzyme class. There are six major classes at the top level according to the reaction they carried out and sub-classes at the lower levels are further specific reactions of these classes. The aim of this thesis is to build a three-level classification model based on the hierarchical structure of EC classes. ENZYME database is used to extract the information of EC classes and enzymes are assigned to these EC classes. Primary sequences of enzymes extracted from UniProtKB/Swiss-Prot database are used to extract features. A subsequence based feature extraction method, Subsequence Profile Map (SPMap) is used in this study. SPMap is a method that explicitly models the differences between positive and negative examples. SPMap pays attention to the conserved subsequences of protein sequences in the same class. SPMap generates the feature vector of each sample protein as a probability of fixed-length subsequences of this protein with respect to a probabilistic profile matrix calculated by clustering similar subsequences in the training dataset. In our case, positive and negative training datasets are prepared for each class, at each level of the tree structure. Subsequence Profile Map (SPMap) is used for feature extraction and Support Vector Machines (SVMs) are used for classification. Five-fold cross validation is used to test the performance of the system. The overall sensitivity, specificity and AUC values for the six major EC classes are 93.08%, 98.95% and 0.993, respectively. The results at the second- and third- levels are also promising.

Subject Keywords

Computer enginnering., Computer software.

URI

http://etd.lib.metu.edu.tr/upload/2/12610969/index.pdf
https://hdl.handle.net/11511/19023

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

A. G. Yaman, “Prediction of enzyme classes in a hierarchical approach by using spmap,” M.S. - Master of Science, Middle East Technical University, 2009.