Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 6802832, 10 pages
Research Article

ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2Shanghai University of Medicine & Health Sciences, Shanghai 201318, China

Received 1 June 2016; Revised 15 July 2016; Accepted 7 August 2016

Academic Editor: Dariusz Mrozek

Copyright © 2016 Daozheng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.