An ensemble approach to protein fold classification by integration of template‐based assignment and support vector machine classifier

Motivation: 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. Results: 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. Availability and Implementation: http://yanglab.nankai.edu.cn/TA‐fold/ Contact: yangjy@nankai.edu.cn or mhb‐506@163.com Supplementary information: Supplementary data are available at Bioinformatics online.