Prediction of Metal-Binding Site Residues Using Support Vector Machine

• Main Authors: Jau-Ji Lin, 林肇基
• Other Authors: Jenn-Kang Hwang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/82371337700374814949

碩士 === 國立交通大學 === 生物資訊研究所 === 93 === Correct identification and analysis of the metal-binding site provides useful clues to the modeling and designing of the binding site in proteins for industrial and therapeutic purposes. As the number of the biological data is rapidly accumulated, the use of machine learning approach to do the prediction becomes more reliable now than ever. We have developed a method using support vector machine (SVM) to predict the metal-binding site residues in proteins containing metal ions. The information used to encode the site residues includes sequence profiles and structural features. The results show that the use of buffer zone can effectively improve the true positive rate (TPR) of the prediction. On five-fold cross-validation, we obtain an average prediction accuracy of 97.4% and 46.2% TPR at a 5% false positive rate (FPR). The results indicate that the use of SVM with suitable coding schemes is an effective way to predict the metal-binding sites in proteins.