A novel predictor for protein structural class based on integrated information of the secondary structure sequence

• Interaction between H lied in different position is considered in this study.
• Information of position of α-helices and β-strands is used to construct feature.
• Consistently high accuracy is obtained by two tests on different datasets.
• Our method is promising to predict structural class for low-similarity dataset.

The structural class has become one of the most important features for characterizing the overall folding type of a protein and played important roles in many aspects of protein research. At present, it is still a challenging problem to accurately predict protein structural class for low-similarity sequences. In this study, an 18-dimensional integrated feature vector is proposed by fusing the information about content and position of the predicted secondary structure elements. The consistently high accuracies of jackknife and 10-fold cross-validation tests on different low-similarity benchmark datasets show that the proposed method is reliable and stable. Comparison of our results with other methods demonstrates that our method is an effective computational tool for protein structural class prediction, especially for low-similarity sequences.

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