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JACIII Vol.20 No.3 pp. 402-411
doi: 10.20965/jaciii.2016.p0402
(2016)

Paper:

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

Liang Kong*,**, Lingfu Kong**, and Rong Jing**

*School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology
Qinhuangdao, China

**School of Information Science and Engineering, Yanshan University
Qinhuangdao, China

Received:
April 24, 2015
Accepted:
December 25, 2015
Published:
May 19, 2016
Creative Commons License
Keywords:
protein domains, secondary protein structure, protein sequence similarity, support vector machines, position specific scoring matrices
Abstract
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.
Cite this article as:
L. Kong, L. Kong, and R. Jing, “Improving the Prediction of Protein Structural Class for Low-Similarity Sequences by Incorporating Evolutionaryand Structural Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.3, pp. 402-411, 2016.
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