Introduction: Neddylation is a highly dynamic and reversible post-translational modification.
The abnormality of neddylation has previously been shown to be closely related to some human
diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of
Objective: As the detection of the lysine neddylation sites by the traditional experimental method is
often expensive and time-consuming, it is imperative to design computational methods to identify
Methods: In this study, a bioinformatics tool named NeddPred is developed to identify underlying
protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and
a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance
in the prediction.
Results: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver
operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms
existing lysine neddylation sites predictor NeddyPreddy.
Conclusion: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation
sites. A user-friendly webserver for NeddPred is accessible at 220.127.116.11/NeddPred/.