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Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification

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Abstract

A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.

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Acknowledgments

We thank Songyot Nakariyakul and Luonan Chen for kindly supporting the dataset. The work is financially supported by the National Natural Science Foundation of China (No. 30871614) and Tianjin Natural Science Foundation (No. 08JCYBJC04100).

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, College of Life Science, Nankai University, Weijin Road 94, Tianjin, 300071, China

    LiQiang Wang & CuiFeng Li

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  1. LiQiang Wang
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  2. CuiFeng Li
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Correspondence to CuiFeng Li.

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Wang, L., Li, C. Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification. Biotechnol Lett 36, 1963–1969 (2014). https://doi.org/10.1007/s10529-014-1577-3

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  • DOI: https://doi.org/10.1007/s10529-014-1577-3

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