Improving the Prediction of Protein Structural Class for Low-Similarity Sequences by Incorporating Evolutionary and Structural Information

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  • Kong Liang
    School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology School of Information Science and Engineering, Yanshan University
  • Kong Lingfu
    School of Information Science and Engineering, Yanshan University
  • Jing Rong
    School of Information Science and Engineering, Yanshan University

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<p>Protein structural class prediction is beneficial to study protein function, regulation and interactions. However, protein structural class prediction for low-similarity sequences (i.e., below 40% in pairwise sequence similarity) remains a challenging problem at present. In this study, a novel computational method is proposed to accurately predict protein structural class for low-similarity sequences. This method is based on support vector machine in conjunction with integrated features from evolutionary information generated with position specific iterative basic local alignment search tool (PSI-BLAST) and predicted secondary structure. Various prediction accuracies evaluated by the jackknife tests are reported on two widely-used low-similarity benchmark datasets (25PDB and 1189), reaching overall accuracies 89.3% and 87.9%, which are significantly higher than those achieved by state-of-the-art in protein structural class prediction. The experimental results suggest that our method could serve as an effective alternative to existing methods in protein structural classification, especially for low-similarity sequences.</p>

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