title: An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.
abstract: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before.
  
  
  
  We developed two algorithms, HH-fold and SVM-fold for protein fold classification. HH-fold is a template-based fold assignment algorithm using the HHsearch program. SVM-fold is a support vector machine-based ab-initio classification algorithm, in which a comprehensive set of features are extracted from three complementary sequence profiles. These two algorithms are then combined, resulting to the ensemble approach TA-fold. We performed a comprehensive assessment for the proposed methods by comparing with ab-initio methods and template-based threading methods on six benchmark datasets. An accuracy of 0.799 was achieved by TA-fold on the DD dataset that consists of proteins from 27 folds. This represents improvement of 5.4-11.7% over ab-initio methods. After updating this dataset to include more proteins in the same folds, the accuracy increased to 0.971. In addition, TA-fold achieved >0.9 accuracy on a large dataset consisting of 6451 proteins from 184 folds. Experiments on the LE dataset show that TA-fold consistently outperforms other threading methods at the family, superfamily and fold levels. The success of TA-fold is attributed to the combination of template-based fold assignment and ab-initio classification using features from complementary sequence profiles that contain rich evolution information.
  
  
  
  http://yanglab.nankai.edu.cn/TA-fold/.
  
  
  
  yangjy@nankai.edu.cn or mhb-506@163.com.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Physics, Northeast Forestry University, Harbin, China.
authors.forename: Jiaqi
authors.initials: J
authors.lastname: Xia
authors.affiliation: Center for Applied Mathematics, Tianjin University, Tianjin, China.
authors.forename: Zhenling
authors.initials: Z
authors.lastname: Peng
authors.affiliation: Department of Physics, Northeast Forestry University, Harbin, China.
authors.forename: Dawei
authors.initials: D
authors.lastname: Qi
authors.affiliation: Department of Physics, Northeast Forestry University, Harbin, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Mu
authors.affiliation: School of Mathematical Sciences, Nankai University, Tianjin, China.
authors.forename: Jianyi
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000145:classification / Q000378:metabolism*; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins