Ju Zhe, Wang Shi-Yun
College of Science, Shenyang Aerospace University, Shenyang110136, P.R. China.
Curr Genomics. 2019 Dec;20(8):592-601. doi: 10.2174/1389202921666191223154629.
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 protein neddylation.
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.
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.
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.
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/.
Neddylation是一种高度动态且可逆的翻译后修饰。先前已表明Neddylation异常与某些人类疾病密切相关。检测Neddylation位点对于阐明蛋白质Neddylation的调控机制至关重要。
由于传统实验方法检测赖氨酸Neddylation位点通常昂贵且耗时，因此设计计算方法来识别Neddylation位点势在必行。
在本研究中，开发了一种名为NeddPred的生物信息学工具来识别潜在的蛋白质Neddylation位点。使用双轮廓贝叶斯特征提取对Neddylation位点进行编码，并利用模糊支持向量机模型来克服预测中的噪声和类不平衡问题。
NeddPred的马修斯相关系数达到0.7082，受试者工作特征曲线下面积为0.9769。独立测试表明，NeddPred明显优于现有的赖氨酸Neddylation位点预测器NeddyPreddy。
因此，NeddPred可以作为现有Neddylation位点预测工具的补充。可通过123.206.31.171/NeddPred/访问NeddPred的用户友好型网络服务器。
