Disulfide Bonding State Prediction with SVM Based on Protein Types

• Main Authors: Chih-Ying Lin, 林志穎
• Other Authors: Chang-Biau Yang
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/60590833620769648648

碩士 === 國立中山大學 === 資訊工程學系研究所 === 98 === Disulfide bonds play crucial roles to predict the three-dimensional structure and the function of a protein. This thesis develops two algorithms to predict the disulfide bonding state of each cysteine in a protein sequence. These methods are based on the multi-stage framework and the multi-classifier of the support vector machine (SVM). The first algorithm achieves 94.0% accuracy of cysteine state prediction for dataset PDB4136, but in some datasets the results are not as good as our expectation. Thus the second algorithm is designed to improve the predicting ability for the proteins which have oxidized and reduced cysteines simultaneously. In addition, a new training strategy is also developed to increase the prediction accuracy. It appends the probabilities which are obtained from the SVM to the existing features and then starts a new training procedure repeatedly to get better performance. The experiments are performed on the datasets derived from well-known databases, such as Protein Data Bank and SWISS-PROT. It gets 94.3% accuracy for predicting disulfide bonding state on dataset PDB4136, which gets improvement 3.6% compared with the previously best result 90.7%.