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Supersecondary structures (SSSs) are the building blocks of protein 3D structures. Accurate prediction of SSSs can be one important step toward building a tertiary structure from the specified secondary structure. How to improve the accuracy of prediction of SSSs by effectively incorporating the sequence order effects is an important and challenging problem. Based on a different form of Chou's pseudo amino acid composition, a novel approach for feature representation of SSSs is proposed. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are incorporated to represent the compositional features of proteins. Each supersecondary structural motif is characterized as a vector of 36 dimensions. In addition, we propose a novel prediction system by using SVM and IDQD algorithm as classifiers. Our method is trained and tested on ArchDB40 dataset containing 3088 proteins. The highest overall accuracy for the training dataset and the independent testing dataset are 77.7 and 69.4%, respectively., (Copyright © 2010 Wiley Periodicals, Inc.)

On the basis of the features of protein sequential pattern, we used the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to predict beta-hairpins motifs in protein sequences. Three rules are used to extract the raw beta-beta motifs sequential patterns for fixed-length. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are combined to represent the compositional features. Eighteen feature variables on a sequential pattern to be predicted are defined in terms of ID. They are integrated in a single formal framework given by IDQD. The method is trained and tested on ArchDB40 dataset containing 3088 proteins. The overall accuracy of prediction and Matthew's correlation coefficient for the independent testing dataset are 81.7% and 0.60, respectively. In addition, a higher accuracy of 84.5% and Matthew's correlation coefficient of 0.68 for the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nucleic Acids Res 2005, 33, 154), which contains 2088 proteins. For a fair assessment of our method, the performance is also evaluated on all 63 proteins used in CASP6. The overall accuracy of prediction is 74.2% for the independent testing dataset., (2009 Wiley Periodicals, Inc.)