Introduction:
Neddylation is a highly dynamic and reversible post-translatiNeddylation 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
protein neddylation.onal modification which has been found to be involved in various biological processes and closely associated with many diseases. The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict neddylation sites. In this study, a novel predictor named NeddPred is proposed to predict lysine neddylation sites. An effective feature extraction method, bi-profile bayes encoding, is employed to encode neddylation sites. Moreover, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of neddylation sites. As illustrated by 10-fold cross-validation, NeddPred achieves an excellent performance with a Matthew's correlation coefficient of 0.7082 and an area under receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing neddylation sites predictor NeddyPreddy. Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly web-server for NeddPred is established at 123.206.31.171/NeddPred/.
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
neddylation sites.
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 123.206.31.171/NeddPred/.
Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components
