Pupylation site detection software tools | Post-translational modification data analysis
As one important post-translational modification of prokaryotic proteins, pupylation plays a key role in regulating various biological processes. The accurate identification of pupylation sites is crucial for understanding the underlying mechanisms of pupylation.
Determines pupylation sites in prokaryotic proteins. GPS-PUP provides a platform build around a group-based prediction system algorithm and a manual curation of scientific literature. The program can be run through a web application for basic research or as a standalone program for large-scale prediction. It intends to furnish results for further analysis dealing with molecular mechanisms of pupylation.
A sequence-based prediction method capable of predicting pupylation sites with probability scores for prioritizing promising pupylation sites. The iPUP is constructed by using SVM, CKSAAP and the large dataset of pupylation sites extracted from PupDB as the classifier, encoding scheme and dataset, respectively. The AUC performances of iPUP on the training and independent test datasets are 0.83 and 0.66, respectively.
Predicts pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites.
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.
Identifies candidate pupylation sites based on the local sequence information. Although the number of experimentally determined pupylation sites will be growing in the future and these sites will be added to our training set to improve predictor performance, the current accuracy of PupPred is useful for predicting novel pupylation substrates as well as new sites in already known substrates.
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