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	Information of protein 3-dimensional (3D) structures plays an essential role in molecular biology, cell biology, biomedicine, and drug design. Accordingly, predicting the 3D structure of a protein is an important problem in Bioinformatics. In this paper, we propose a novel taxonomic method, called PFPA. It is featured by combining a comprehensive feature set through an ensemble classifier. The proposed feature set sufficiently explores the discriminatory sequence evolution information from the profile of PSI-BLAST, and the local and global secondary structure information from the profile of PSI-PRED. As compared with existing taxonomic methods on a benchmark dataset (called DD), PFPA successfully improves the prediction accuracy to 73.6%, which is 2.1% higher than the best accuracy ever reported. The significant improvement suggests the superiority of PFPA. Moreover, PFPA performs well without significant performance degradation on three updated large-scale datasets, which suggests the robustness and generalization of PFPA. More details of PFPA can be seen in the paper. The procedure of how PFPA works can be seen below.

Information of protein 3-dimensional (3D) structures plays an essential role in molecular biology, cell biology, biomedicine, and drug design. Accordingly, predicting the 3D structure of a protein is an important problem in Bioinformatics.

In this paper, we propose a novel taxonomic method, called PFPA. It is featured by combining a comprehensive feature set through an ensemble classifier. The proposed feature set sufficiently explores the discriminatory sequence evolution information from the profile of PSI-BLAST, and the local and global secondary structure information from the profile of PSI-PRED. As compared with existing taxonomic methods on a benchmark dataset (called DD), PFPA successfully improves the prediction accuracy to 73.6%, which is 2.1% higher than the best accuracy ever reported. The significant improvement suggests the superiority of PFPA. Moreover, PFPA performs well without significant performance degradation on three updated large-scale datasets, which suggests the robustness and generalization of PFPA.

More details of PFPA can be seen in the paper. The procedure of how PFPA works can be seen below.