swMATH ID: 41918
Software Authors: Yin, Shuwan; Zheng, Jia; Jia, Cangzhi; Zou, Quan; Lin, Zhengkui; Shi, Hua
Description: UPFPSR: a ubiquitylation predictor for plant through combining sequence information and random forest. As one of the most significant protein post-translational modifications (PTMs) in eukaryotes, ubiquitylation plays an essential role in regulating diverse cellular functions, such as apoptosis, cell division, DNA repair and replication, intracellular transport and immune reactions. Traditional experimental methods have the defect of being time-consuming, costly and labor-intensive. Therefore, it is highly desired to develop automated computational methods that can recognize potential ubiquitylation sites rapidly and accurately. In this study, we propose a novel predictor, named UPFPSR, for predicting lysine ubiquitylation sites in plant. UPFPSR is developed using multiple physicochemical properties of amino acids and sequence-based statistical information. In order to find a suitable classification algorithm, four traditional algorithms and two deep learning networks are compared, and the random forest with superior performance is selected ultimately. An extensive benchmarking shows that UPFPSR outperforms the most advanced ubiquitylation prediction tool on each measurement indicator, with the accuracy of 77.3
Homepage: https://www.aimspress.com/aimspress-data/mbe/2022/1/PDF/mbe-19-01-035.pdf
Source Code:  https://github.com/ysw-sunshine/UPFPSR
Keywords: protein post-translational modifications; lysine ubiquitylation; traditional machine learning; deep learning; test evaluation
Related Software: hCKSAAP_UbSite; DeepVF; iFeature; GitHub; PseAAC
Cited in: 1 Publication

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