| Summary: | 碩士 === 中華大學 === 資訊工程學系碩士班 === 94 === As the Human Genome Project (HGP) progresses, there are more biological data available. Many proteins and genes have been sequenced, but their functions remain unknown. Therefore, function predicting methods become important.
It is recognized that molecular structure of a protein is closely related to its function. In this thesis, we develop sequence-structure alignment method to predict protein function. This method uses protein sequence and secondary structure to build a statistical HMM model which is trained according to pre-existing databases. In this paper, the function is classified by Gene Ontology (GO), and the protein data files are collected from Protein Data Bank (PDB).
Two predicted algorithms are adopted. The first one builds different HMM models based on function classification of GO, and then determines overall probability of protein belonging to this function. The accuracy of this algorithm reaches to 63%. However, the protein may have one or more functions. The second algorithm builds additional HMM model using the data not belong to this category. This algorithm can predict multiple functions of an unknown protein. Its accuracy is raised to 81%.
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