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, School of Software, Central South University, Changsha, 410075, School of Software, Central South University, Changsha, 410075, School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo, 315100, Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008
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.
