Prediction of Protein S-Sulfenylation Sites Using a Deep Belief Network

Title:Prediction of Protein S-Sulfenylation Sites Using a Deep Belief Network

Volume: 13 Issue: 5

Author(s): Lulu Nie, Lei Deng*, Chao Fan, Weihua Zhan and Yongjun Tang

Affiliation:

• School of Software, Central South University, Changsha, 410075,China

Keywords: Deep belief network, support vector machine, S-sulfenylation sites, restricted boltzmann machines.

Abstract: Background: Protein S-Sulfenylation, the reversible oxidative modification of cysteine thiol groups to cysteine S-Sulfenic acids, is a post-translational modification (PTM) that plays a critical role in regulating protein function and signal transduction. The identification of specific protein Ssulfenylation sites is crucial to understand the underlying molecular mechanisms.

Objective: We sought to develop a computational method that can effectively predict S-sulfenylation sites by using optimally extracted properties.

Method: We propose DBN-Sulf, which uses a Deep Belief Network (DBN) with Restricted Boltzmann Machines (RBMs) to reduce the feature dimensions from a combination of heterogeneous information, including amino acid related features, evolutionary features, and structure-based features. Then a support vector machine (SVM) based predictor is built with the optimal features.

Results: We evaluate the DBN-Sulf classifier using a training dataset including 1007 positive sites and 7837 negative sites with 5-fold cross validation, and get an AUC score of 0.80, an ACC of 0.85 and a MCC of 0.53, which are significantly better than that of the existing methods. We further validate our method on the independent test set and obtain promising results.

Conclusion: The superior performance over existing S-sulfenylation site prediction approaches indicates the importance of the deep belief network-based feature extracting procedure.

Cite this article as:

Nie Lulu, Deng Lei*, Fan Chao, Zhan Weihua and Tang Yongjun, Prediction of Protein S-Sulfenylation Sites Using a Deep Belief Network, Current Bioinformatics 2018; 13 (5) . https://dx.doi.org/10.2174/1574893612666171122152208