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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

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

Author(s): Wangren Qiu, Chunhui Xu, Xuan Xiao and Dong Xu*

Volume 20, Issue 5, 2019

Page: [389 - 399] Pages: 11

DOI: 10.2174/1389202919666191014091250

Price: $65

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: Ubiquitination, machine learning, random forest, protein annotation, subcellular localization, functional domain.

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