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

Abstract EN

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.