Improvement of Protein All-atom Prediction with SVM

• Main Authors: Hsin-Wei Yen, 顏欣偉
• Other Authors: Chang-Biau Yang
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/63490224280046629490

碩士 === 國立中山大學 === 資訊工程學系研究所 === 98 === There are many studies have been devoted to solve the all-atom protein back- bone reconstruction problem (PBRP), such as Adcock’s method, MaxSprout, SAB- BAC and Chang’s method. In the previous work, Wang et al. tried to solve this problem by homology modeling. Then, Chang et al. improved Wang’s result by refining the positions of oxygen based on the AMBER force field. We compare the results in CASP7 and 8 from Chang et al. and SABBAC v1.2 and find that some proteins get better predicting results by Chang’s method and others do better in SABBAC. Based on SVM, we propose a tool preference classification method for determining which tool is potentially the better one for predicting the structure of a target protein. We design a series of steps to select the better feature sets for SVM. Our method is tested on the proteins with standard amino acids in CASP7 and 8 dataset, which contains 30 and 24 protein sequences, respectively. The experimen- tal results show that our method has 7.39% and 2.94% RMSD improvement against Chang’s result in CASP7 and 8, respectively. Our method can also be applied to other effective prediction methods, even if they will be developed in the future.