title: SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites.
abstract: BACKGROUND
  Protein S-sulfenylation is a type of post-translational modification (PTM) involving the covalent binding of a hydroxyl group to the thiol of a cysteine amino acid. Recent evidence has shown the importance of S-sulfenylation in various biological processes, including transcriptional regulation, apoptosis and cytokine signaling. Determining the specific sites of S-sulfenylation is fundamental to understanding the structures and functions of S-sulfenylated proteins. However, the current lack of reliable tools often limits researchers to use expensive and time-consuming laboratory techniques for the identification of S-sulfenylation sites. Thus, we were motivated to develop a bioinformatics method for investigating S-sulfenylation sites based on amino acid compositions and physicochemical properties.
  
  
  RESULTS
  In this work, physicochemical properties were utilized not only to identify S-sulfenylation sites from 1,096 experimentally verified S-sulfenylated proteins, but also to compare the effectiveness of prediction with other characteristics such as amino acid composition (AAC), amino acid pair composition (AAPC), solvent-accessible surface area (ASA), amino acid substitution matrix (BLOSUM62), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM). Various prediction models were built using support vector machine (SVM) and evaluated by five-fold cross-validation. The model constructed from hybrid features, including PSSM and physicochemical properties, yielded the best performance with sensitivity, specificity, accuracy and MCC measurements of 0.746, 0.737, 0.738 and 0.337, respectively. The selected model also provided a promising accuracy (0.693) on an independent testing dataset. Additionally, we employed TwoSampleLogo to help discover the difference of amino acid composition among S-sulfenylation, S-glutathionylation and S-nitrosylation sites.
  
  
  CONCLUSIONS
  This work proposed a computational method to explore informative features and functions for protein S-sulfenylation. Evaluation by five-fold cross validation indicated that the selected features were effective in the identification of S-sulfenylation sites. Moreover, the independent testing results demonstrated that the proposed method could provide a feasible means for conducting preliminary analyses of protein S-sulfenylation. We also anticipate that the uncovered differences in amino acid composition may facilitate future studies of the extensive crosstalk among S-sulfenylation, S-glutathionylation and S-nitrosylation.
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. vanminha9@gmail.com.
authors.forename: Van-Minh
authors.initials: VM
authors.lastname: Bui
authors.affiliation: Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsin-Chu, 300, Taiwan. a4467@ms7.mmh.org.tw.|Mackay Junior College of Medicine, Nursing and Management, Taipei, 112, Taiwan. a4467@ms7.mmh.org.tw.|Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan. a4467@ms7.mmh.org.tw.
authors.forename: Shun-Long
authors.initials: SL
authors.lastname: Weng
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. love0501tw@gmail.com.
authors.forename: Cheng-Tsung
authors.initials: CT
authors.lastname: Lu
authors.affiliation: Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, 110, Taiwan. kevinchang1025@gmail.com.
authors.forename: Tzu-Hao
authors.initials: TH
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. julweng@saturn.yzu.edu.tw.|Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 320, Taiwan. julweng@saturn.yzu.edu.tw.
authors.forename: Julia Tzu-Ya
authors.initials: JT
authors.lastname: Weng
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. francis@saturn.yzu.edu.tw.|Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 320, Taiwan. francis@saturn.yzu.edu.tw.
authors.forename: Tzong-Yi
authors.initials: TY
authors.lastname: Lee
mesh_terms: D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D056510:Position-Specific Scoring Matrices; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
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