Positive-Unlabeled Learning for Pupylation Sites Prediction

Abstract

Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict 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.

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

“…The detailed process of the algorithm is described as follows (Stage 1 is our proposed part. Stage 2 and Stage 3 are the same as PUL-PUP [ 14 ]):…”

“…To evaluate the effectiveness of the proposed method for pupylation site prediction, we compare EPuL with other methods, including PUL-PUP [ 14 ], PSoL [ 13 ], and SVM balance on the training dataset. In PSoL [ 13 ] algorithms, a two-class SVM is applied to filter the negative set from the non-annotated lysine sites and expand the negative set at each iteration.…”

“…In PSoL [ 13 ] algorithms, a two-class SVM is applied to filter the negative set from the non-annotated lysine sites and expand the negative set at each iteration. Additionally, in PUL-PUP [ 14 ] algorithms, the non-annotated lysine sites are treated as unlabeled samples and positive-unlabeled learning technique is used to predict of pupylation sites. The difference for us is on the selection of the initial negative set.…”

“…Though the independent testing dataset was highly imbalanced, it can reflect the real effectiveness of different methods. Similar to the current pupylation site prediction methods [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ], the sliding window method was adopted to encode each sample in the dataset. The window size was set to 21 here, in accordance with [ 9 ].…”

“…Wand et al [ 13 ] employed the non-annotated lysine sites as unlabeled training samples and then used a two-class SVM to expand reliable negative set at each iteration. More recently, Jiang et al [ 14 ] applied the positive-unlabeled learning technique to the prediction of pupylation sites, which combined the SVM and CKSAAP to construct the predictor PUL-PUP.…”