Prediction of protein structural class with Rough Sets

In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729–738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches.The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics.Because there is a gap between sequence and structure, the prediction of protein structural classes is still a hot research field today. One protein usually can be classified into one of the four structural classes: all-α, all-β, α/β and α+β. Many different algorithms and efforts have been made to address this problem so far. A review about prediction of protein structural class and subcellular locations by Chou [1] presented this problem systematically, and introduced and compared some existing methods.In 1986, Klein and Delisi [2] first put forward the prediction of protein structural classes, and shortly afterward, Klein [3] brought discriminate analysis method to this problem. A new weighting method [4] was proposed to predict protein structural classes from amino acid composition in 1992. After that, another new method, called maximum component coefficient method, was proposed by Zhang and Chou [5], which had a higher correct rate than other methods. Later, a new neural networks based algorithm [6] was developed that considers six hydrophobic amino acid patterns together with amino acid compositions, and a cross-validation test was used to verify the accuracy of this method. Chou [7] brought a novel approach to predict protein structural class in a (20-1)-D amino acid composition space, which takes into account the coupling effect among different amin