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 protein neddylation. 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/.

中文翻译：

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通过 Chou 的 5 步规则和一般伪组件使用双剖面贝叶斯特征提取识别赖氨酸内化位点

引言 Neddylation 是一种高度动态和可逆的翻译后修饰。neddylation的异常先前已被证明与一些人类疾病密切相关。neddylation 位点的检测对于阐明蛋白质 neddylation 的调控机制至关重要。目的由于传统的实验方法检测赖氨酸neddylation位点往往成本高、耗时长，因此设计计算方法识别neddylation位点势在必行。方法 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别不平衡问题。结果 NeddPred 的 Matthew 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 显着优于现有的赖氨酸 neddylation 位点预测因子 NeddyPreddy。结论 因此，NeddPred 可以补充现有的预测 neddylation 位点的工具。NeddPred 的用户友好型网络服务器可在 123.206.31.171/NeddPred/ 访问。