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Prediction of protein structural classes based on feature selection technique

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Abstract

The prediction of protein structural classes is beneficial to understanding folding patterns, functions and interactions of proteins. In this study, we proposed a feature selection-based method to accurately predict protein structural classes. Three datasets with sequence identity lower than 25% were used to test the prediction performance of the method. Through jackknife cross-validation, we have verified that the overall accuracies of these three datasets are 92.1%, 89.7% and 84.0%, respectively. The proposed method is more efficient and accurate than other existing methods. The present study will offer an excellent alternative to other methods for predicting protein structural classes.

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Authors and Affiliations

  1. Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Hui Ding, Hao Lin, Feng-Biao Guo, Jian Huang & Nini Rao

  2. Department of Physics, Center for Genomics and Computational Biology, College of Sciences, Hebei United University, Tangshan, 063000, China

    Wei Chen

  3. School of information and Engineering, Sichuan Agricultural University, Yaan, 625014, China

    Zi-Qiang Li

Authors
  1. Hui Ding
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  2. Hao Lin
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  3. Wei Chen
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  4. Zi-Qiang Li
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  5. Feng-Biao Guo
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  6. Jian Huang
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  7. Nini Rao
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Correspondence to Hao Lin or Wei Chen.

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Ding, H., Lin, H., Chen, W. et al. Prediction of protein structural classes based on feature selection technique. Interdiscip Sci Comput Life Sci 6, 235–240 (2014). https://doi.org/10.1007/s12539-013-0205-6

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  • DOI: https://doi.org/10.1007/s12539-013-0205-6

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