Abstract
Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific score matrix (PSSM) with auto covariance (AC) transformation, is introduced. Thus, a sample protein is represented by a series of discrete components, which could partially incorporate the long-range sequence order information and evolutionary information reflected from the PSI-BLAST profile. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides the state-of-the-art performance for structural class prediction. A Web server that implements the proposed method is freely available at http://202.194.133.5/xinxi/AAC_PSSM_AC/index.htm.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 10731040), Shanghai Leading Academic Discipline Project (No. S30405), Innovation Program of Shanghai Municipal Education Commission (No. 09zz134), and the Science and Technology Project of Provincial Education Department of Shandong of China (No. J08LI09).
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Liu, T., Geng, X., Zheng, X. et al. Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles. Amino Acids 42, 2243–2249 (2012). https://doi.org/10.1007/s00726-011-0964-5
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DOI: https://doi.org/10.1007/s00726-011-0964-5