| Summary: | 碩士 === 國立中央大學 === 生物資訊與系統生物研究所 === 96 === The study of protein thermostability plays an important role in both basic and applied research. Most of the studies on protein thermostability are focused on the analysis of structure or sequence comparison among homologous proteins, and identify the factors that affect the protein thermostability. Scientists had found key properties that influence protein thermostability, such as amino acid composition, hydrophobic interaction, and ionic interaction, etc. However, the properties correlate to psychrophilic properties of proteins are less studied. The purpose of this study is to analyze the properties of selected pools of proteins by developing a method to predict the thermostability or psychrophilicity. Furthermore, to identify which are the key features We used the data provided by NCBI prokaryotic genome project to select 86470 proteins and the temperature data, the optimal growth temperatures from the source prokaryotes, followed by calculation of protein features by feature selection algorithm. Finally, the vital factors related to temperatures, amino acid composition, dipeptide composition, pseudo amino acid composition are selected. A machine learning method is performed to build a robust prediction model on protein thermostability and psychrophilicity. We believed these three types of amino acid composition have a significant effect on protein temperature classification.
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