title: Predicting protein fold pattern with functional domain and sequential evolution information.
abstract: The fold pattern of a protein is one level deeper than its structural classification, and hence is more challenging and complicated for prediction. Many efforts have been made in this regard, but so far all the reported success rates are still under 70%, indicating that it is extremely difficult to enhance the success rate even by 1% or 2%. To address this problem, here a novel approach is proposed that is featured by combining the functional domain information and the sequential evolution information through a fusion ensemble classifier. The predictor thus developed is called PFP-FunDSeqE. Tests were performed for identifying proteins among their 27 fold patterns. Compared with the existing predictors tested by a same stringent benchmark dataset, the new predictor can, for the first time, achieve over 70% success rate. The PFP-FunDSeqE predictor is freely available to the public as a web server at http://www.csbio.sjtu.edu.cn/bioinf/PFP-FunDSeqE/.
authors.affiliation: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China. hbshen@sjtu.edu.cn
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000818:Animals; D005075:Biological Evolution; D030562:Databases, Protein; D008954:Models, Biological; D008956:Models, Chemical; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins