Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components

• Published in: Current genomics Vol. 20; no. 8; pp. 592 - 601
• Main Authors: Ju, Zhe, Wang, Shi-Yun
• Format: Journal Article
• Language: English
• Published: United Arab Emirates Bentham Science Publishers Ltd 01.12.2019; Benham Science Publishers; Bentham Science Publishers
• Subjects: Bayesian analysis; Bioinformatics; Computer applications; Correlation coefficient; Correlation coefficients; Experimental methods; Feature extraction; Identification methods; Lysine; Noise prediction; Post-translation; Proteins; Support vector machines; Post-translational modification; fuzzy support vector machine; neddylation; chou’s 5-steps rule; feature extraction; pseudo components
• ISSN: 1389-2029; 1875-5488
• DOI: 10.2174/1389202921666191223154629

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/.