Guo-Cheng Lei, Jijun Tang and Pu-Feng Du* Pages 665 - 672 ( 8 )
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.
S-sulfenylation sites, physicochemical properties difference, partition the training set, voting scheme, sequence segments, ensemble classifier.
School of Computer Science and Technology, Tianjin University, Tianjin 300350, School of Computer Science and Technology, Tianjin University, Tianjin 300350, School of Computer Science and Technology, Tianjin University, Tianjin 300350