Predicting S-sulfenylation Sites Using Physicochemical Properties Differences

Title:Predicting S-sulfenylation Sites Using Physicochemical Properties Differences

Volume: 14 Issue: 9

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

Affiliation:

• School of Computer Science and Technology, Tianjin University, Tianjin 300350,China

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

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.

Cite this article as:

Lei Guo-Cheng, Tang Jijun and Du Pu-Feng*, Predicting S-sulfenylation Sites Using Physicochemical Properties Differences, Letters in Organic Chemistry 2017; 14 (9) . https://dx.doi.org/10.2174/1570178614666170421164731