A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.
Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.
Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
Ming
M
Fan
Hunan Mechanical and Electrical Polytechnic, Chang Sha 410151, China.
Bin
B
Zheng
Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
Lihua
L
Li
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
D011506:Proteins
Ada-Boost; Protein structural class prediction; multi-agent; multi-classifier fusion; secondary structural sequence