Predicting S-sulfenylation Sites Using Physicochemical Properties Differences

Author(s): Guo-Cheng Lei, Jijun Tang, Pu-Feng Du*.

Journal Name: Letters in Organic Chemistry

Volume 14 , Issue 9 , 2017

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Abstract:

Protein S-sulfenylation plays a critical role in pathology and physiology. Detecting S-sulfenylated proteins in cells is of great value in medical and life sciences. Several computational methods have been developed to predict S-sulfenylation sites. However, the prediction performances are still not ideal.

Method: We developed a computational method to predict S-sulfenylation sites by utilizing physicochemical property differences to represent sequence segments around S-sulfenylation sites. By using a clustering method to partition the training set, we developed a novel prediction method using an ensemble classifier.

Results: Our method achieves an overall accuracy of 69.88% on the benchmarking dataset. We compared our method to the other state-of-the-art methods. Our method performs better than all existing methods.

Conclusion: We proposed a computational method to predict S-sulfenylated sites, which outperforms other state-of-the-art methods.

Keywords: S-sulfenylation sites, physicochemical properties difference, partition the training set, voting scheme, sequence segments, ensemble classifier.

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Article Details

VOLUME: 14
ISSUE: 9
Year: 2017
Page: [665 - 672]
Pages: 8
DOI: 10.2174/1570178614666170421164731
Price: $58

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