Background: As a new type of protein acylation modification, lysine glutarylation has been
found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the
biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation
sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are
treated as positive samples and non-verified lysine sites as the negative samples to train predictors.
However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally
Methods: In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas
the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named
PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm.
Results: Experimental results show that PUL-GLU significantly outperforms the current glutarylation
site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation
Conclusion: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.