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Computational Prediction of Ubiquitination Proteins Using Evolutionary Profiles and Functional Domain Annotation

[ Vol. 20 , Issue. 5 ]

Author(s):

Wangren Qiu, Chunhui Xu, Xuan Xiao and Dong Xu*   Pages 389 - 399 ( 11 )

Abstract:


Background: Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis, and localization. Identification of ubiquitination proteins is of fundamental importance for understanding the molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well-studied model organisms.

Objective: To reduce experimental costs, computational methods have been introduced to predict ubiquitination sites, but the accuracy is unsatisfactory. If it can be predicted whether a protein can be ubiquitinated or not, it will help in predicting ubiquitination sites. However, all the computational methods so far can only predict ubiquitination sites.

Methods: In this study, the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction has been developed. The method extracts features from sequence conservation information through a grey system model, as well as functional domain annotation and subcellular localization.

Results: Together with the feature analysis and application of the relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 90.13%, with Matthew’s correlation coefficient of 80.34%. The predicted results on an independent test data achieved 87.71% as accuracy and 75.43% of Matthew’s correlation coefficient, better than the prediction from the best ubiquitination site prediction tool available.

Conclusion: Our study may guide experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The code is available at: https://github.com/Chunhuixu/UBIPredic_QWRCHX.

Keywords:

Ubiquinaon, Machine learning, Random forest, Protein annotaon, Subcellular localizaon

Affiliation:

Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333046, Informatics Institute, University of Missouri, Columbia, MO 65201, Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333046, Informatics Institute, University of Missouri, Columbia, MO 65201



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