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%, precision of 75%, recall of 81.7%, F1-score of 0.7824, and AUC of 0.84 on the independent test dataset. The results indicate that UPFPSR can provide new guidance for further experimental study on ubiquitylation. The data sets and source code used in this study are freely available at https://github.com/ysw-sunshine/UPFPSR.

O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine.

O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.