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Improved Prediction of Protein Binding Sites from Sequences Using Genetic Algorithm

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Abstract

We undertook this project in response to the rapidly increasing number of protein structures with unknown functions in the Protein Data Bank. Here, we combined a genetic algorithm with a support vector machine to predict protein–protein binding sites. In an experiment on a testing dataset, we predicted the binding sites for 66% of our datasets, made up of 50 testing hetero-complexes. This classifier achieved greater sensitivity (60.17%), specificity (58.17%), accuracy (64.08%), and F-measure (54.79%), and a higher correlation coefficient (0.2502) than those of the support vector machine. This result can be used to guide biologists in designing specific experiments for protein analysis.

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Abbreviations

PDB:

Protein Data Bank

FP:

False positive

SVM:

Support vector machine

FN:

False negative

GA/SVM:

Genetic algorithm and support vector machine

CC:

Correlation coefficient

TP:

True positive

TN:

True negative

HSSP:

Homology-derived secondary structure of protein

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Acknowledgments

We would like to thank Dr. Chih-Jen Lin from the National Taiwan University for providing the original LIBSVM tool. This work was supported by the Project of the Provincial Natural Scientific Fund of the Bureau of Education of Anhui Province (KJ2007B239) and the Project of the Doctoral Foundation of the Ministry of Education, China. (200403057002).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, 230039, Hefei, China

    Xiuquan Du, Jiaxing Cheng & Jie Song

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  1. Xiuquan Du
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  2. Jiaxing Cheng
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  3. Jie Song
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Correspondence to Xiuquan Du.

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Du, X., Cheng, J. & Song, J. Improved Prediction of Protein Binding Sites from Sequences Using Genetic Algorithm. Protein J 28, 273–280 (2009). https://doi.org/10.1007/s10930-009-9192-1

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