期刊
PLANT MOLECULAR BIOLOGY
卷 110, 期 1-2, 页码 81-92出版社
SPRINGER
DOI: 10.1007/s11103-022-01288-3
关键词
Ubiquitination sites; Arabidopsis thaliana; Sequence information; Support vector machine
资金
- Natural Science Foundation of Henan province [212300410367, 202300410102]
- Science and Technology Research Key Project of Educational Department of Henan Province [21A520023, 22A520030]
- National Natural Science Foundation of China [62072160, 62076089, 62072157]
- Key Project of Science and Technology Department of Henan Province [212102310381]
- Educational Science Research Foundation of Henan Normal University [2018JK19]
- Teaching Reform Research and Practice Project of Henan Normal University [201936]
- Production and Learning Cooperation and Cooperative Education Project of Ministry of Education of China [202102633006]
- Key Project of Science and Technology Department of Xinxiang city [GG2021004]
- National Project Cultivation Fund Project of Henan Normal University [2020PL12]
This study proposes a calculation method that can effectively detect ubiquitination sites in Arabidopsis thaliana. The method achieves promising performances using support vector machine learning classifiers and various feature extraction methods, outperforming previous methods. Additionally, the study provides in-depth analysis of the physicochemical properties of amino acids near ubiquitination sites.
Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants. It can be said that the calculation and prediction of Arabidopsis thaliana ubiquitination sites is still in its infancy. Based on this, we describe a calculation method, PseAraUbi (Prediction of Arabidopsis thaliana ubiquitination sites using pseudo amino acid composition), that can effectively detect ubiquitination sites on Arabidopsis thaliana using support vector machine learning classifiers. Based on protein sequence information, extract features from the Chou-Fasman parameter, amino acids hydrophobicity features, polarity information and selected for classification with the Boruta algorithm. PseAraUbi achieves promising performances with an AUC score of 0.953 with fivefold cross-validation on the training dataset, which are significantly better than that of the pioneer Arabidopsis thaliana ubiquitination sites method. We also proved the ability of our proposed method on independent test sets, thus gaining a competitive advantage. In addition, we also in-depth analyzed the physicochemical properties of amino acids in the region adjacent to the ubiquitination site. To facilitate the community, the source code, optimal feature subset, ubiquitination sites dataset in the Arbidopsis proteome are available at GitHub (https://github.com/HNUBioinformati cs/PseAraUbi.git) for interest users.
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