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A bioinformatics tool for accurate prediction of pupylation sites. IMP–PUP is constructed on the composition of k-spaced amino acid pairs and trained with a modified semi-supervised self-training support vector machine (SVM) algorithm. The proposed algorithm iteratively trains a series of support vector machine classifiers on both annotated and non-annotated pupylated proteins. Independent tests also show that IMP–PUP significantly outperforms three other existing pupylation site predictors: GPS–PUP, iPUP, and pbPUP.

pbPUP / profile-based PUpylation site Predictor

A computational tool to predict pupylation sites based on protein sequence information. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a support vector machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset.


Serves as a powerful tool to help identify pupylation sites in prokaryotic proteins. The large-scale prediction and functional analysis results can be used for the further investigation of molecular mechanisms of pupylation. From small-scale and large-scale studies, we collected 238 potentially pupylated substrates for which the exact pupylation sites were still not determined. As an example application, we predicted B85% of these proteins with at least one potential pupylation site.