Title:Computational Identification of Lysine Glutarylation Sites Using Positive- Unlabeled Learning
VOLUME: 21 ISSUE: 3
Author(s):Zhe Ju* and Shi-Yun Wang
Affiliation:College of Science, Shenyang Aerospace University, Shenyang 110136, College of Science, Shenyang Aerospace University, Shenyang 110136
Keywords:Post-translational modification, glutarylation, support vector machine, positive-unlabeled learning, protein acylation,
site predictors.
Abstract:
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
identified yet.
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
sites.
Conclusion: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.