title: Prediction of neddylation sites from protein sequences and sequence-derived properties.
abstract: BACKGROUND
  Neddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features.
  
  
  RESULTS
  We have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysine-centred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites.
  
  
  CONCLUSIONS
  In this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.
authors.forename: Ahmet Sinan
authors.initials: AS
authors.lastname: Yavuz
authors.forename: Namık Berk
authors.initials: NB
authors.lastname: Sözer
authors.forename: Osman Uğur
authors.initials: OU
authors.lastname: Sezerman
mesh_terms: D000595:Amino Acid Sequence; D019540:Area Under Curve; D019295:Computational Biology; D008239:Lysine / Q000737:chemistry / Q000378:metabolism; D056510:Position-Specific Scoring Matrices; D011499:Protein Processing, Post-Translational; D012372:ROC Curve; D060388:Support Vector Machine; D014452:Ubiquitins / Q000737:chemistry / Q000378:metabolism
chemical_list: D014452:Ubiquitins; D008239:Lysine