Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

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 si

SUBMITTER: Ju Z 

PROVIDER: S-EPMC7521029 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature